Methods and systems for identifying brain disorders

ABSTRACT

Methods and systems for determining whether brain tissue is indicative of a disorder, such as a neurodegenerative disorder, are provided. The methods and systems generally utilize data processing techniques to assess a level of congruence between measured parameters obtained from magnetic resonance imaging (MRI) data and simulated parameters obtained from computational modeling of brain tissues.

CROSS-REFERENCE

This application is a Continuation of U.S. patent application Ser. No.15/987,794, filed May 23, 2018, which is a Continuation of InternationalApplication No. PCT/US2017/064745, filed Dec. 5, 2017, which claims thebenefit of U.S. Provisional Patent Application Ser. No. 62/430,351,filed Dec. 6, 2016, and U.S. Provisional Patent Application Ser. No.62/481,839, filed Apr. 5, 2017, each of which is entirely incorporatedherein by reference for all purposes.

BACKGROUND

Neurodegenerative diseases leading to dementia are a tremendous societalburden, currently devastating 9 million people domestically and 47million people worldwide. Current inability to effectively prevent,diagnose and combat neurodegeneration results in staggering direct andindirect costs. Alzheimer's disease (AD), the most common cause ofdementia, alone afflicts over 5 million Americans and accounts for the6th leading cause of death in the USA. AD requires an estimated 18billion hours of unpaid caretaking and well over $250 billion of medicalcosts annually. Prevalence of the disease is projected to escalate tonearly 14 million people domestically and 135 million worldwide by 2050,with no potential cure in immediate sight. There is a dire need fortechnological advancements toward diagnostics, prevention, therapeuticsand eventual cures that will each have profound beneficial impacts onthe population.

Current clinical evaluation typically includes non-invasive brainimaging with magnetic resonance imaging (MRI), positron emissiontomography (PET), or other advanced imaging strategies which provideinsight into tissue volume changes, chemical composition, corticalmetabolic rate, alterations associated with tissue cellularity anddisease biomarkers, and structural abnormalities attributed toneurodegenerative disease. To aid in the diagnosis of AD anddifferential diagnosis from non-Alzheimer dementias, fluorodeoxyglucose(FDG) PET and amyloid PET reveal AD-associated patterns of cerebralcortical metabolism and beta amyloid deposits in the gray matter,respectively. Similarly, tau PET reveals neurofibrillary tangles in thebrain. However, due to a lack of advancement in analysis technologies,meaningful use of these imaging techniques for neurodegenerative diseaseis restricted to late stages when considerable tissue damage andcognitive or other clinical abnormalities are present. As we deepen ourunderstanding of the multiplicity of abnormalities associated with AD,there is increasing evidence that the continual targeting of theseamyloid plaques and neurofibrillary tangles may merely be treating latestage symptoms rather than the underlying causes. The inability toeffectively detect early stages of AD precludes pre-symptomaticintervention and conceals the potential beneficial effects of drugcandidates.

Implicit to the neurodegenerative process is the death of the signalingnerve cells in the brain, though this can merely be the ultimateconsequence in a cascade of degeneration within brain tissue. Thestructural integrity of tissue is necessary for neuron support andsurvival and clearance of molecular waste that must be removed from thebrain for maintenance of neural tissue homeostasis and efficientfunction. Alterations in non-cellular components of the brain arecomplicit in the degenerative process and may be a precursor of lostnerve cell function. It has been shown that proper regulation of neuraltissue homeostasis is necessary for eliminating toxic residue buildup, aprocess that can be altered in the AD brain. Yet, there remains limitedunderstanding of brain structural content and its impact on transport ofmolecules in the brain interstitium. Currently, the clinical use and thediagnostic capacity of brain MRI remains limited to differentialdiagnosis, only after symptomatic presentation, principally due to theinherently low spatial resolution—MRI image voxels are in mm dimensions,whereas structural changes contributing to tissue degeneration originateat the sub-micron scale. FDG, amyloid, and tau PET scans suffer fromsimilar limitations.

SUMMARY

Recognized herein is a need for tools that allow early detection ofAlzheimer's disease and other neurodegenerative disorders, includingtools that may utilize approaches that detect microscopic changes inbrain tissue from low-resolution magnetic resonance imaging (MRI) scans.Such approaches may leverage a deeper understanding of brain tissuemicrostructure to more reliably predict and interpret the health of thebrain from MRI scans well before severe tissue damage irreversiblyimpedes healthy cognitive function.

Provided herein is an image analysis platform that can detect andquantify brain tissue abnormality (such as neurodegeneration) in everyvoxel of standard clinical brain MRI. The platform may provide detailedinformation about the brain tissue health at the microscopic level andthe resulting observed patterns of pathologic involvement that iscurrently missing in the neuroimaging/brain diagnostics field. As aresult, complex brain diseases, such as Alzheimer's disease, which arecurrently diagnosed very late (i.e., at the late symptomatic stages),may be diagnosed or otherwise identified prior to the onset of advancedsymptoms. The platform may allow early-stage testing of novel drugcandidates for clinical trials, which have previously failed due to poorpatient selection, late intervention, and very high trial costs. All ofthese factors may be significantly improved using the platform.

Provided herein are methods and systems for determining whether braintissue is indicative of a disorder, such as a neurodegenerativedisorder. The methods and systems may allow the early diagnosis of abrain disorder much earlier than would be possible using prior methodsand systems, such as many years before the development of symptomsassociated with the disorder that are detectable using prior methods andsystems. The methods and systems may provide high accuracy in diagnosinga brain disorder (such as greater than 90% accuracy), as measured by avariety of criteria described herein.

The methods and systems of the present disclosure may utilize dataprocessing techniques to assess a level of congruence between measuredparameters obtained from magnetic resonance imaging (MRI) data andsimulated parameters obtained from computational modeling of braintissues. The methods and systems generally operate by determining alevel of congruence between the one or more measured parameters and theone or more simulated parameters for one or more voxels of the MRI data.The simulated parameters are obtained from a plurality ofmicrostructural models. Each microstructural model of the plurality ofmicrostructural models is obtained by subjecting a microstructural modelthat is not indicative of a disorder to a series of microstructuralperturbations. After assessing the level of congruence between the oneor more measured parameters and the one or more simulated parameters fora number of microstructural models of the plurality of microstructuralmodels, a diagnostic microstructural model that meets a thresholdcongruence is selected. The diagnostic microstructural model is used todetermine the disorder state of the brain tissue associated with thevoxel.

The methods and systems may be applied to a plurality of voxels of theMRI data, such that a level of congruence is determined for each voxelof the plurality of voxels. In this manner, a diagnostic model and adisorder state may be determined for each voxel of the plurality ofvoxels. The methods and systems may be applied to determine a diagnosticmodel and a disorder state for a plurality of voxels located within aparticular region of a brain, within a whole brain, or across aplurality of brains from a plurality of subjects.

In an aspect, a method for determining a disorder state of brain tissuein a brain of a subject may comprise: (a) obtaining magnetic resonanceimaging (MRI) data comprising at least one MRI image of the brain, theMRI image comprising a plurality of voxels, a voxel of the plurality ofvoxels being associated with the brain tissue of the brain of thesubject and comprising one or more measured MRI parameters in the MRIdata; (b) for the voxel of the plurality of voxels, using one or morecomputer processors to process the one or more measured MRI parameterswith one or more simulated MRI parameters for the voxel, the one or moresimulated MRI parameters being generated from one or moremicrostructural models at the voxel; (c) for the voxel of the pluralityof voxels, selecting a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (d) for the voxel of the plurality of voxels, using the diagnosticmodel to determine the disorder state of the brain tissue associatedwith the voxel.

Each voxel may comprise a plurality of measured MRI parameters. The oneor more measured MRI parameters may be a plurality of measured MRIparameters. The one or more simulated MRI parameter may be a pluralityof simulated MRI parameters.

The method may further comprise repeating (b)-(d) one or more times foradditional voxels of the plurality of voxels. The method may furthercomprise repeating (b)-(d) for all other voxels of the plurality ofvoxels. The method may further comprise repeating (b)-(d) for all voxelsassociated with a specified region of the brain. The method may furthercomprise repeating (b)-(d) for all voxels associated with an entirety ofthe brain. The method may further comprise repeating (a)-(d) for aplurality of MRI images, each MRI image of the plurality of MRI imagesassociated with a brain selected from a plurality of brains, each brainof the plurality of brains associated with a subject selected from aplurality of subjects.

The MRI image may be selected from the group consisting of: alongitudinal relaxation time (T1)-weighted MRI image, a transverserelaxation time (T2)-weighted MRI image, and a diffusion-weighted MRIimage. The measured MRI parameter may be selected from the groupconsisting of: a longitudinal relaxation time (T1), a transverserelaxation time (T2), and a diffusion coefficient. The simulated MRIparameter may be selected from the group consisting of: a longitudinalrelaxation time (T1), a transverse relaxation time (T2), and a diffusioncoefficient.

The one or more microstructural models may comprise informationregarding a parameter selected from the group consisting of:intracellular content, extracellular content, distribution ofextracellular content within interstitial space, distribution ofintracellular content within intracellular space, and tissue geometry.The one or more microstructural models may comprise measured orpredicted values of a parameter selected from the group consisting of:cell density, cell shape, cell geometry, cell size, cell distribution,intercellular spacing, extracellular matrix homogeneity, interstitialtortuosity, water to protein ratio, water to lipid ratio, water tocarbohydrate ratio, protein to lipid ratio, protein to carbohydrateratio, and lipid to carbohydrate ratio. The one or more microstructuralmodels may be selected from a microstructural model library. Themicrostructural model library may comprise at least 100 microstructuralmodels.

The microstructural model library may be constructed by: (a) creating afirst microstructural model corresponding to a brain state that is notassociated with a disorder; and (b) iteratively subjecting the firstmicrostructural model to a perturbation, each iteration producing anadditional perturbed microstructural model. (b) may comprise subjectingthe first microstructural model to at least 100 iterations to generateat least 100 perturbed microstructural models. The first microstructuralmodel may be selected based on knowledge of the brain region associatedwith the voxel. The perturbation may comprise an operation selected fromthe group consisting of: depleting cells, altering cellular morphologyor distribution, altering intracellular or interstitial physico-chemicalcomposition or distribution, altering extracellular matrix compositionor distribution, and altering intercellular spacing. The perturbationmay comprise a stochastic procedure.

The threshold congruence may be determined by computing an objectivefunction between the one or more measured MRI parameters and the one ormore simulated MRI parameters. The objective function may comprise an L1norm or an L2 norm.

Determining the disorder state of the brain tissue associated with thevoxel may be achieved at an accuracy of at least 90%. Determining thedisorder state across the brain tissue associated with the specifiedregion of the brain may be achieved at an accuracy of at least 90%.Determining the disorder state of the brain tissue associated with thewhole brain of the subject may be achieved at an accuracy of at least90%. Determining the disorder state of the brain tissue associated withthe plurality of subjects may be achieved at an accuracy of at least90%.

The disorder may be a non-neurodegenerative disorder. The disorder maybe selected from the group consisting of: a primary neoplasm, ametastatic neoplasm, a seizure disorder, a seizure disorder with focalcortical dysplasia, a demyelinating disorder, a non-neurodegenerativeencephalopathy, a cerebrovascular disease, and a psychological disorder.The disorder may be a neurodegenerative disorder. The disorder may beselected from the group consisting of: Alzheimer's disease, anon-Alzheimer's dementia disorder, Parkinson's disease, a Parkinsonismdisorder, a motor neuron disease, Huntington's disease, a Huntington'sdisease-like syndrome, transmissible spongiform encephalopathy, chronictraumatic encephalopathy, and a tauopathy.

The method may enable diagnosis of a neurodegenerative disorder morethan 5 years prior to the development of symptoms associated with theneurodegenerative disorder. The method may enable monitoring of theneurodegenerative disorder at a plurality of time points, the pluralityof time points separated by a plurality of time intervals.

The method may further comprise constructing a brain map that, for eachvoxel of the plurality of voxels, indicates the disorder state of thebrain tissue associated with the voxel. The method may further comprisedisplaying the brain map on a graphical user interface of an electronicdevice of a user. The brain map may comprise a qualitative abnormalitymap. The brain map may comprise a binary abnormality map. The brain mapmay comprise a quantitative abnormality map. The brain map may comprisea percent abnormality map.

In an aspect, a method for determining a disorder state of a tissue in aportion of a body of a subject may comprise: obtaining magneticresonance imaging (MRI) data comprising at least one MRI image of thetissue, the MRI image comprising a plurality of voxels, a voxel of theplurality of voxels being associated with the tissue of the subject andcomprising one or more measured MRI parameters in the MRI data; (b) forthe voxel of the plurality of voxels, using one or more computerprocessors to process the one or more measured MRI parameters with oneor more simulated MRI parameters for the voxel, the one or moresimulated MRI parameters being generated from one or moremicrostructural models at the voxel; (c) for the voxel of the pluralityof voxels, selecting a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (d) for the voxel of the plurality of voxels, using the diagnosticmodel to determine the disorder state of the tissue associated with thevoxel.

The tissue may be selected from the group consisting of: spinal cordtissue, heart tissue, vascular tissue, lung tissue, liver tissue, kidneytissue, esophageal tissue, stomach tissue, intestinal tissue, pancreatictissue, thyroid tissue, adrenal tissue, spleen tissue, lymphatic tissue,appendix tissue, breast tissue, bladder tissue, vaginal tissue, ovariantissue, uterine tissue, penile tissue, testicular tissue, prostatictissue, skeletal muscle tissue, skin, and non-brain tissue of the headand neck.

In an aspect, a non-transitory computer-readable medium may comprisemachine-executable code that, upon execution by one or more computerprocessors, implements a method for detecting a disorder state of braintissue in a brain of a subject, the method comprising: (a) obtainingmagnetic resonance imaging (MRI) data comprising at least one MRI imageof the brain, the MRI image comprising a plurality of voxels, a voxel ofthe plurality of voxels being associated with the brain tissue of thebrain of the subject and comprising one or more measured MRI parametersin the MRI data; (b) for the voxel of the plurality of voxels, using oneor more computer processors to process the one or more measured MRIparameters with one or more simulated MRI parameters for the voxel, theone or more simulated MRI parameters being generated from one or moremicrostructural models at the voxel; (c) for the voxel of the pluralityof voxels, selecting a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (d) for the voxel of the plurality of voxels, using the diagnosticmodel to determine the disorder state of the brain tissue associatedwith the voxel.

Each voxel may comprise a plurality of measured MRI parameters. The oneor more measured MRI parameters may be a plurality of measured MRIparameters. The one or more simulated MRI parameter may be a pluralityof simulated MRI parameters.

The method may further comprise repeating (b)-(d) one or more times foradditional voxels of the plurality of voxels. The method may furthercomprise repeating (b)-(d) for all other voxels of the plurality ofvoxels. The method may further comprise repeating (b)-(d) for all voxelsassociated with a specified region of the brain. The method may furthercomprise repeating (b)-(d) for all voxels associated with an entirety ofthe brain. The method may further comprise repeating (a)-(d) for aplurality of MRI images, each MRI image of the plurality of MRI imagesassociated with a brain selected from a plurality of brains, each brainof the plurality of brains associated with a subject selected from aplurality of subjects.

The MRI image may be selected from the group consisting of: alongitudinal relaxation time (T1)-weighted MRI image, a transverserelaxation time (T2)-weighted MRI image, and a diffusion-weighted MRIimage. The measured MRI parameter may be selected from the groupconsisting of: a longitudinal relaxation time (T1), a transverserelaxation time (T2), and a diffusion coefficient. The simulated MRIparameter may be selected from the group consisting of: a longitudinalrelaxation time (T1), a transverse relaxation time (T2), and a diffusioncoefficient.

The one or more microstructural models may comprise informationregarding a parameter selected from the group consisting of:intracellular content, extracellular content, distribution ofextracellular content within interstitial space, distribution ofintracellular content within intracellular space, and tissue geometry.The one or more microstructural models may comprise measured orpredicted values of a parameter selected from the group consisting of:cell density, cell shape, cell geometry, cell size, cell distribution,intercellular spacing, extracellular matrix homogeneity, interstitialtortuosity, water to protein ratio, water to lipid ratio, water tocarbohydrate ratio, protein to lipid ratio, protein to carbohydrateratio, and lipid to carbohydrate ratio. The one or more microstructuralmodels may be selected from a microstructural model library. Themicrostructural model library may comprise at least 100 microstructuralmodels.

The microstructural model library may be constructed by: (a) creating afirst microstructural model corresponding to a brain state that is notassociated with a disorder; and (b) iteratively subjecting the firstmicrostructural model to a perturbation, each iteration producing anadditional perturbed microstructural model. (b) may comprise subjectingthe first microstructural model to at least 100 iterations to generateat least 100 perturbed microstructural models. The first microstructuralmodel may be selected based on knowledge of the brain region associatedwith the voxel. The perturbation may comprise an operation selected fromthe group consisting of: depleting cells, altering cellular morphologyor distribution, altering intracellular or interstitial physico-chemicalcomposition or distribution, altering extracellular matrix compositionor distribution, and altering intercellular spacing. The perturbationmay comprise a stochastic procedure.

The threshold congruence may be determined by computing an objectivefunction between the one or more measured MRI parameters and the one ormore simulated MRI parameters. The objective function may comprise an L1norm or an L2 norm.

Determining the disorder state of the brain tissue associated with thevoxel may be achieved at an accuracy of at least 90%. Determining thedisorder state across the brain tissue associated with the specifiedregion of the brain may be achieved at an accuracy of at least 90%.Determining the disorder state of the brain tissue associated with thewhole brain of the subject may be achieved at an accuracy of at least90%. Determining the disorder state of the brain tissue associated theplurality of subjects may be achieved at an accuracy of at least 90%.

The disorder may be a non-neurodegenerative disorder. The disorder maybe selected from the group consisting of: a primary neoplasm, ametastatic neoplasm, a seizure disorder, a seizure disorder with focalcortical dysplasia, a demyelinating disorder, a non-neurodegenerativeencephalopathy, a cerebrovascular disease, and a psychological disorder.The disorder may be a neurodegenerative disorder. The disorder may beselected from the group consisting of: Alzheimer's disease, anon-Alzheimer's dementia disorder, Parkinson's disease, a Parkinsonismdisorder, a motor neuron disease, Huntington's disease, a Huntington'sdisease-like syndrome, transmissible spongiform encephalopathy, chronictraumatic encephalopathy, and a tauopathy.

The method may enable diagnosis of a neurodegenerative disorder morethan 5 years prior to the development of symptoms associated with theneurodegenerative disorder. The method may enable monitoring of theneurodegenerative disorder at a plurality of time points, the pluralityof time points separated by a plurality of time intervals.

The method may further comprise constructing a brain map that, for eachvoxel of the plurality of voxels, indicates the disorder state of thebrain tissue associated with the voxel. The method may further comprisedisplaying the brain map on a graphical user interface of an electronicdevice of a user. The brain map may comprise a qualitative abnormalitymap. The brain map may comprise a binary abnormality map. The brain mapmay comprise a quantitative abnormality map. The brain map may comprisea percent abnormality map.

In an aspect, a non-transitory computer-readable medium may comprisemachine-executable code that, upon execution by one or more computerprocessors, implements a method for detecting a disorder state of braintissue in a brain of a subject, the method comprising: obtainingmagnetic resonance imaging (MRI) data comprising at least one MRI imageof the tissue, the MRI image comprising a plurality of voxels, a voxelof the plurality of voxels being associated with the tissue of thesubject and comprising one or more measured MRI parameters in the MRIdata; (b) for the voxel of the plurality of voxels, using one or morecomputer processors to process the one or more measured MRI parameterswith one or more simulated MRI parameters for the voxel, the one or moresimulated MRI parameters being generated from one or moremicrostructural models at the voxel; (c) for the voxel of the pluralityof voxels, selecting a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (d) for the voxel of the plurality of voxels, using the diagnosticmodel to determine the disorder state of the tissue associated with thevoxel.

The tissue may be selected from the group consisting of: spinal cordtissue, heart tissue, vascular tissue, lung tissue, liver tissue, kidneytissue, esophageal tissue, stomach tissue, intestinal tissue, pancreatictissue, thyroid tissue, adrenal tissue, spleen tissue, lymphatic tissue,appendix tissue, breast tissue, bladder tissue, vaginal tissue, ovariantissue, uterine tissue, penile tissue, testicular tissue, prostatictissue, skeletal muscle tissue, skin, and non-brain tissue of the headand neck.

In an aspect, a system for determining a disorder state of brain tissuein a brain of a subject may comprise: (a) a database comprising magneticresonance imaging (MRI) data comprising at least one MRI image of thebrain, the MRI image comprising a plurality of voxels, a voxel of theplurality of voxels being associated with the brain tissue of the brainof the subject and comprising a measured MRI parameter in the MRI data;and (b) one or more computer processors operatively coupled to thedatabase, wherein the one or more computer processors are individuallyor collectively programmed to: (i) for the voxel of the plurality ofvoxels, use one or more computer processors to process the one or moremeasured MRI parameters with one or more simulated MRI parameters forthe voxel, the one or more simulated MRI parameters being generated fromone or more microstructural models at the voxel; (ii) for the voxel ofthe plurality of voxels, select a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (iii) for the voxel of the plurality of voxels, use the diagnosticmodel to determine the disorder state of the brain tissue associatedwith the voxel.

Each voxel may comprise a plurality of measured MRI parameters. The oneor more measured MRI parameters may be a plurality of measured MRIparameters. The one or more simulated MRI parameter may be a pluralityof simulated MRI parameters.

The one or more computer processors may be further individually orcollectively programmed to repeat (i)-(iii) one or more times foradditional voxels of the plurality of voxels. The one or more computerprocessors may be further individually or collectively programmed torepeat (i)-(iii) for all other voxels of the plurality of voxels. Theone or more computer processors may be further individually orcollectively programmed to repeat (i)-(iii) for all voxels associatedwith a specified region of the brain. The one or more computerprocessors may be further individually or collectively programmed torepeat (i)-(iii) for all voxels associated with an entirety of thebrain. The one or more computer processors may be further individuallyor collectively programmed to repeat (i)-(iii) for a plurality of MRIimages, each MRI image of the plurality of MRI images associated with abrain selected from a plurality of brains, each brain of the pluralityof brains associated with a subject selected from a plurality ofsubjects.

The MRI image may be selected from the group consisting of: alongitudinal relaxation time (T1)-weighted MRI image, a transverserelaxation time (T2)-weighted MRI image, and a diffusion-weighted MRIimage. The measured MRI parameter may be selected from the groupconsisting of: a longitudinal relaxation time (T1), a transverserelaxation time (T2), and a diffusion coefficient. The simulated MRIparameter may be selected from the group consisting of: a longitudinalrelaxation time (T1), a transverse relaxation time (T2), and a diffusioncoefficient.

The one or more microstructural models may comprise informationregarding a parameter selected from the group consisting of:intracellular content, extracellular content, distribution ofextracellular content within interstitial space, distribution ofintracellular content within intracellular space, and tissue geometry.The one or more microstructural models may comprise measured orpredicted values of a parameter selected from the group consisting of:cell density, cell shape, cell geometry, cell size, cell distribution,intercellular spacing, extracellular matrix homogeneity, interstitialtortuosity, water to protein ratio, water to lipid ratio, water tocarbohydrate ratio, protein to lipid ratio, protein to carbohydrateratio, and lipid to carbohydrate ratio. The one or more microstructuralmodels may be selected from a microstructural model library. Themicrostructural model library may comprise at least 100 microstructuralmodels.

The microstructural model library may be constructed by: (a) creating afirst microstructural model corresponding to a brain state that is notassociated with a disorder; and (b) iteratively subjecting the firstmicrostructural model to a perturbation, each iteration producing anadditional perturbed microstructural model. (b) may comprise subjectingthe first microstructural model to at least 100 iterations to generateat least 100 perturbed microstructural models. The first microstructuralmodel may be selected based on knowledge of the brain region associatedwith the voxel. The perturbation may comprise an operation selected fromthe group consisting of: depleting cells, altering cellular morphologyor distribution, altering intracellular or interstitial physico-chemicalcomposition or distribution, altering extracellular matrix compositionor distribution, and altering intercellular spacing. The perturbationmay comprise a stochastic procedure.

The threshold congruence may be determined by computing an objectivefunction between the one or more measured MRI parameters and the one ormore simulated MRI parameters. The objective function may comprise an L1norm or an L2 norm.

Determining the disorder state of the brain tissue associated with thevoxel may be achieved at an accuracy of at least 90%. Determining thedisorder state across the brain tissue associated with the specifiedregion of the brain may be achieved at an accuracy of at least 90%.Determining the disorder state of the brain tissue associated with thewhole brain of the subject may be achieved at an accuracy of at least90%. Determining the disorder state of the brain tissue associated theplurality of subjects may be achieved at an accuracy of at least 90%.

The disorder may be a non-neurodegenerative disorder. The disorder maybe selected from the group consisting of: a primary neoplasm, ametastatic neoplasm, a seizure disorder, a seizure disorder with focalcortical dysplasia, a demyelinating disorder, a non-neurodegenerativeencephalopathy, a cerebrovascular disease, and a psychological disorder.The disorder may be a neurodegenerative disorder. The disorder may beselected from the group consisting of: Alzheimer's disease, anon-Alzheimer's dementia disorder, Parkinson's disease, a Parkinsonismdisorder, a motor neuron disease, Huntington's disease, a Huntington'sdisease-like syndrome, transmissible spongiform encephalopathy, chronictraumatic encephalopathy, and a tauopathy.

The system may enable diagnosis of a neurodegenerative disorder morethan 5 years prior to the development of symptoms associated with theneurodegenerative disorder. The system may enable monitoring of theneurodegenerative disorder at a plurality of time points, the pluralityof time points separated by a plurality of time intervals.

The one or more computer processors may be further individually orcollectively programmed to construct a brain map that, for each voxel ofthe plurality of voxels, indicates the disorder state of the braintissue associated with the voxel. The one or more computer processorsmay be further individually or collectively programmed to display thebrain map on a graphical user interface of an electronic device of auser. The brain map may comprise a qualitative abnormality map. Thebrain map may comprise a binary abnormality map. The brain map maycomprise a quantitative abnormality map. The brain map may comprise apercent abnormality map.

In an aspect, a system for determining a disorder state of a tissue in aportion of a body of a subject may comprise: (a) a database comprisingmagnetic resonance imaging (MRI) data comprising at least one MRI imageof the brain, the MRI image comprising a plurality of voxels, a voxel ofthe plurality of voxels being associated with the brain tissue of thebrain of the subject and comprising a measured MRI parameter in the MRIdata; and (b) one or more computer processors operatively coupled to thedatabase, wherein the one or more computer processors are individuallyor collectively programmed to: (i) for the voxel of the plurality ofvoxels, use one or more computer processors to process the one or moremeasured MRI parameters with one or more simulated MRI parameters forthe voxel, the one or more simulated MRI parameters being generated fromone or more microstructural models at the voxel; (ii) for the voxel ofthe plurality of voxels, select a diagnostic model from the one or moremicrostructural models, the diagnostic model meeting a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model;and (iii) for the voxel of the plurality of voxels, use the diagnosticmodel to determine the disorder state of the tissue associated with thevoxel.

The tissue may be selected from the group consisting of: spinal cordtissue, heart tissue, vascular tissue, lung tissue, liver tissue, kidneytissue, esophageal tissue, stomach tissue, intestinal tissue, pancreatictissue, thyroid tissue, adrenal tissue, spleen tissue, lymphatic tissue,appendix tissue, breast tissue, bladder tissue, vaginal tissue, ovariantissue, uterine tissue, penile tissue, testicular tissue, prostatictissue, skeletal muscle tissue, skin, and non-brain tissue of the headand neck.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1 shows a method for determining a neurological disorder state ofbrain tissue in a brain of a subject.

FIG. 2 shows a method for constructing a microstructural model library.

FIG. 3 shows a method for determining a neurological disorder state ofbrain tissue in a brain of a subject.

FIG. 4 shows a computer system that is programmed or otherwiseconfigured to operate a system or method for determining a disorderstate of brain tissue in a subject.

FIG. 5A shows a portion of a cell array for a normal tissuemicrostructural model.

FIG. 5B shows a sample Monte Carlo simulation with delivery of freelymoving molecules.

FIG. 5C shows representative models of healthy and degenerating braintissue.

FIG. 6 shows the processing of human magnetic resonance imaging (MRI)images to produce neurodegeneration maps.

FIG. 7 shows examples of MRI, percent neurodegeneration (PND), andquantitative neurodegeneration (QND) brain maps from a diseasedindividual.

FIG. 8 shows exemplary brain maps for a young, asymptomatic brain, anormal aged brain with no detected neurodegenerative symptoms, and anaged brain with clinical symptoms of severe neurodegeneration.

FIG. 9 shows mean plots of degenerative analysis output parameters forAlzheimer's Disease Neuroimaging Initiative (ADNI) images.

FIG. 10 shows a population distribution for a longitudinal study toevaluate early detection of Alzheimer's disease (AD).

FIG. 11 shows an early detection analysis of MRI images collectedlongitudinally from the population distribution.

FIG. 12 shows a determination of abnormality in a mixed cohortlongitudinal study.

FIG. 13 shows the registration or alignment of subject images to anannotated human brain parcellation atlas.

FIG. 14 shows optimized single region prediction accuracy of diagnosiswithin the ADNI dataset for a variety of brain regions using the systemsand methods described herein.

FIG. 15 shows PND measurement distributions across subjects of a varietyof ages for the whole brain, cerebellum, thalamus, posterior cingulate,precuneus, and hippocampus.

FIG. 16 shows PND measurement distributions across subjects of a varietyof ages for the entorhinal cortex, basal ganglia, parietal lobe,occipital lobe, prefrontal cortex, and premotor cortex.

FIG. 17 shows PND measurement distributions across subjects of a varietyof ages for the precentral gyms, postcentral gyms, temporal lobe,paracentral lobule, olfactory bulb, and anterior-mid cingulum.

FIG. 18 shows attainable AD diagnostic metrics using machine learning.

FIG. 19 shows a distribution of whole brain scores (WBS) for ADNIsubject scans.

DETAILED DESCRIPTION

While various embodiments of the invention are shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

Where values are described as ranges, it will be understood that suchdisclosure includes the disclosure of all possible sub-ranges withinsuch ranges, as well as specific numerical values that fall within suchranges irrespective of whether a specific numerical value or specificsub-range is expressly stated.

As used herein, the term “subject” generally refers to an animal, suchas a mammalian species (e.g., human) or avian (e.g., bird) species, orother organism, such as a plant. The subject can be a vertebrate, amammal, a mouse, a primate, a simian, or a human. Animals may include,but are not limited to, farm animals, sport animals, and pets. A subjectcan be a healthy or asymptomatic individual, an individual that has oris suspected of having a disease (e.g., a neurological disorder) or apre-disposition to the disease, or an individual that is in need oftherapy or suspected of needing therapy. A subject can be a patient.

As used herein, the term “brain region” (also referred to as “region ofa brain” or “region of the brain”) generally refers to any sub-structureof a brain. The brain region may be a sub-region or the entirety of aprosencephalon (forebrain), or a sub-region or the entirety of amesencephalon (midbrain), or a sub-region or the entirety of arhombencephalon (hindbrain). The brain region may be a medullaoblongata. The brain region may be a medullary pyramid, olivary body,inferior olivary nucleus, rostral ventrolateral medulla, caudalventrolateral medulla, solitary nucleus, respiratory center, dorsalrespiratory group, ventral respiratory group, pre-Botzinger complex,Botzinger complex, retrotrapezoid nucleus, nucleus retrofacialis,nucleus retroambiguus, nucleus paraambiguus, paramedian reticularnucleus, gigantocellular reticular nucleus, parafacial zone, cuneatenucleus, gracile nucleus, perihypoglossal nucleus, intercalated nucleus,prepositus nucleus, sublingual nucleus, area postrema, medullary cranialnerve nucleus, inferior salivatory nucleus, nucleus ambiguus, dorsalnucleus of the vagus nerve, or hypoglossal nucleus. The brain region maybe a pons. The brain region may be a pontine nucleus, pontine cranialnerve nucleus, pontine nucleus of the trigeminal nerve sensory nucleus,motor nucleus for the trigeminal nerve, abducens nucleus,vestibulocochlear nucleus, superior salivatory nucleus, pontinetegmentum, pontine micturition center (Barrington's nucleus), locuscoeruleus, pedunculopontine nucleus, laterodorsal tegmental nucleus,tegmental pontine reticular nucleus, parabrachial area, medialparabrachial nucleus, lateral parabrachial nucleus, subparabrachialnucleus (Kolliker-Fuse nucleus), pontine respiratory group, superiorolivary complex, paramedian pontine reticular formation, parvocellularreticular nucleus, caudal pontine reticular nucleus, medial nucleus ofthe trapezoid body, cerebellar peduncle, superior cerebellar peduncle,middle cerebella peduncle, or inferior cerebellar peduncle. The brainregion may be a cerebellum. The brain region may be a cerebellar vermis,cerebellar hemisphere, anterior lobe, posterior lobe, flocculonodularlobe, interposed nucleus, globose nucleus, emboliform nucleus, ordentate nucleus. The brain region may be a midbrain (mesencephalon). Thebrain region may be a tectum. The brain region may be a corporaquadrigemina, inferior colliculi, or superior colliculi. The brainregion may be a pretectum. The brain region may be a tegmentum. Thebrain region may be a periaqueductal gray, rostral interstitial nucleusof medial longitudinal fasciculus, midbrain reticular formation, dorsalraphe nucleus, red nucleus, ventral tegmental area, parabrachialpigmented nucleus, paranigral nucleus, rostromedial tegmental nucleus,caudal linear nucleus, rostral linear nucleus of the raphe,interfascicular nucleus, substantia nigra, pars compact, parsreticulate, or interpeduncular nucleus. The brain region may be acerebral peduncle. The brain region may be a crus cerebri. The brainregion may be a mesencephalic cranial nerve nucleus. The brain regionmay be an oculomotor nucleus, Edinger-Westphal nucleus, or trochlearnucleus. The brain region may be a mesenchephalic duct (aqueduct ofSylvius). The brain region may be a forebrain (prosencephalon). Thebrain region may be a diencephalon. The brain region may be anepithalamus. The brain region may be a pineal body, habenular nucleus,stria medullaris, or taenia thalami. The brain region may be a thirdventricle. The brain region may be a fourth ventricle. The brain regionmay be a lateral ventricle. The brain region may be a subcommissuralorgan. The brain region may be a thalamus. The brain region may be aanteroventral nucleus, anterodorsal nucleus, anteromedial nucleus,medial nuclear group, medial dorsal nucleus, midline nuclear group,paratenial nucleus, reuniens nucleus, rhomboidal nucleus, intralaminarnuclear group, centromedian nucleus, parafascicular nucleus, paracentralnucleus, central lateral nucleus, central medial nucleus, lateralnuclear group, lateral dorsal nucleus, lateral posterior nucleus,pulvinar, ventral nuclear group, ventral anterior nucleus, ventrallateral nucleus, ventral posterior nucleus, ventral posterior lateralnucleus, ventral posterior medial nucleus, metathalamus, medialgeniculate body, lateral geniculate body, or thalamic reticular nucleus.The brain region may be a hypothalamus. The brain region may be ananterior hypothalamus, medial area of the anterior hypothalamus,anterior medial preoptic area, medial preoptic nucleus, suprachiasmaticnucleus, paraventricular nucleus, supraoptic nucleus, anteriorhypothalamic nucleus, lateral area of the anterior hypothalamus,anterior lateral preoptic area, anterior part of the lateral nucleus,supraoptic nucleus, median preoptic nucleus, periventricular preopticnucleus, tuberal hypothalamus, medial area of the tuberal hypothalamus,dorsomedial hypothalamic nucleus, ventromedial nucleus, arcuate nucleus,lateral area of the tuberal hypothalamus, tuberal part of the lateralnucleus, lateral tuberal nucleus, posterior hypothalamus, medial area ofthe posterior hypothalamus, mammillary nucleus, posterior nucleus,lateral area of the posterior hypothalamus, posterior part of thelateral nucleus, optic chiasm, subfornical organ, periventricularnucleus, pituitary stalk, tuber cinereum, tuberal nucleus, ortuberomammillary nucleus. The brain region may be a subthalamus. Thebrain region may be a subthalamic nucleus or zona incerta. The brainregion may be a pituitary gland. The brain region may be aneurohypophysis, pars intermedia (intermediate lobe), oradenohypophysis. The brain region may be a cerebrum (telencephalon). Thebrain region may be a white matter, centrum semiovale, corona radiate,internal capsule, external capsule, extreme capsule, subcorticalcerebrum, hippocampus (medial temporal lobe), dentate gyrus, cornuammonis, cornu ammonis area 1, cornu ammonis area 2, cornu ammonis area3, cornu ammonis area 4, amygdala (limbic lobe), central nucleus of theamygdala, medial nucleus of the amygdala, cortical nucleus of theamygdala, basomedial nucleus of the amygdala, lateral nucleus of theamygdala, basolateral nucleus of the amygdala, stria terminalis, bednucleus of the stria terminalis, claustrum, basal ganglia, striatum,dorsal striatum (neostriatum), putamen, caudate nucleus, ventralstriatum, nucleus accumbens, olfactory tubercle, globus pallidus,subthalamic nucleus, basal forebrain, anterior perforated substance,substantia innominate, nucleus basalis, diagonal band of Broca, septalnucleus, medial septal nucleus, lamina terminalis, or the vascular organof the lamina terminalis. The brain region may be a rhinencephalon(paleopallium). The brain region may be an olfactory bulb, olfactorytract, anterior olfactory nucleus, piriform cortex, anterior commissure,uncus, or periamygdaloid cortex. The brain region may be a cerebralcortex (neopallium). The brain region may be a frontal lobe, frontallobe cortex, primary motor cortex (precentral gyms), supplementary motorcortex, premotor cortex, prefrontal cortex, orbitofrontal cortex,dorsolateral prefrontal cortex, frontal lobe gyms, superior frontalgyms, middle frontal gyms, inferior frontal gyms, paracentral lobule,Brodmann area 4, Brodmann area 6, Brodmann area 8, Brodmann area 9,Brodmann area 10, Brodmann area 11, Brodmann area 12, Brodmann area 24,Brodmann area 25, Brodmann area 32, Brodmann area 33, Brodmann area 44,Brodmann area 45, Brodmann area 46, Brodmann area 47, parietal lobe,parietal lobe cortex, primary somoatosensory cortex, secondarysomatosensory cortex, posterior parietal cortex, parietal lobe gyms,postcentral gyms, precuneus, posterior cingulate cortex, Brodmann area1, Brodmann area 2, Brodmann area 3, Brodmann area 5, Brodmann area 7,Brodmann area 23, Brodmann area 26, Brodmann area 29, Brodmann area 31,Brodmann area 39, Brodmann area 40, occipital lobe, occipital lobecortex, primary visual cortex, secondary visual cortex, third visualcortex, fourth visual cortex, dorsomedial area, middle temporal visualcortex, occipital lobe gyms, lateral occipital gyms, cuneus, Brodmannarea 17, Brodmann area 18, Brodmann area 19, temporal lobe, temporallobe cortex, primary auditory cortex, secondary auditory cortex,inferior temporal cortex, posterior inferior temporal cortex, temporallobe gyms, superior temporal gyms, middle temporal gyms, inferiortemporal gyms, entorhinal cortex, perirhinal cortex, parahippocampalgyms, fusiform gyms, Brodmann area 20, Brodmann area 21, Brodmann area22, Brodmann area 27, Brodmann area 34, Brodmann area 35, Brodmann area36, Brodmann area 37, Brodmann area 38, Brodmann area 41, Brodmann area42, medial superior temporal area, insular cortex, cingulate cortex,anterior cingulate cortex, retrosplenial cortex, indusium griseum,Brodmann area 23, Brodmann area 24, Brodmann area 26, Brodmann area 29,Brodmann area 30, Brodmann area 31, or Brodmann area 32. The brainregion may be a neural pathway. The brain region may be a superiorlongitudinal fasciculus, arcuate fasciculus, perforant pathway,thalamocortical radiation, corpus callosum, anterior commissure,interthalamic adhesion, posterior commissure, habenular commissure,fornix, mammillotegmental fasciculus, cerebral peduncle, medialforebrain bundle, medial longitudinal fasciculus, myoclonic triangle,major dopaminergic pathway, mesocortical pathway, mesolimbic pathway,nigrostriatal pathway, tuberorinfundibular pathway, serotonin pathway,raphe nucleus, norepinephrine pathway, locus coeruleus, epinephrinepathway, glutamate pathway, or acetylcholine pathway. The brain regionmay be a descending fiber. The brain region may be an extrapyramidalsystem, pyramidal tract, corticospinal tract, lateral corticospinaltract, anterior corticospinal tract, corticopontine fiber, frontopontinefiber, temporopontine fiber, corticobulbar tract, corticomesencephalictract, tectospinal tract, interstitiospinal tract, rubrospinal tract,rubroolivary tract, olivocerebellar tract, olivospinal tract,vestibulospinal tract, lateral vestibulospinal tract, medialvestibulospinal tract, reticulospinal tract, lateral raphespinal tract,alpha system, or gamma system. The brain region may be a somatosensorysystem. The brain region may be a posterior column, medial lemniscuspathway, gracile fasciculus, cuneate fasciculus, medial lemniscus,spinothalamic tract, lateral spinothalamic tract, anterior spinothalamictract, spinomesencephalic tract, spinocerebellar tract, spinoolivarytract, or spinoreticular tract. The brain region may be a visual system.The brain region may be an optic tract or optic radiation. The brainregion may be an auditory system. The brain region may be a trapezoidbody. The brain region may be a lateral lemniscus. The brain region maybe a brain stem. The brain region may be a cranial nerve, terminalnerve, olfactory nerve, optic nerve, oculomotor nerve, trochlear nerve,trigeminal nerve, abducens nerve, facial nerve, vestibulocochlear nerve,glossopharyngeal nerve, vagus nerve, accessory nerve, or hypoglossalnerve. The brain region may be a neurovascular system. The brain regionmay be a middle cerebral artery, posterior cerebral artery, anteriorcerebral artery, vertebral artery, basilar artery, circle of Willis,glymphatic system, venous system, or circumventricular organ. The brainregion may be a meningeal covering, dura mater, arachnoid mater, piamater, epidural space, subdural space, subarachnoid space, arachnoidseptum, superior cistern, cistern of lamina terminalis, chiasmaticcistern, interpeduncular cistern, pontine cistern, cisterna magna, orspinal subarachnoid space. The brain region may comprise any 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400,500, 600, 700, 800, 900, 1,000, or more than 1,000 brain regionsdescribed herein. The brain region may comprise a number of brainregions that is within a range defined by any two of the precedingvalues.

As used herein, the term “tissue” generally refers to biological tissue.The biological tissue may be from a subject.

As used herein, the term “voxel” generally refers to a unit volume inthree-dimensional space. The voxel may correspond to a giventhree-dimensional volume, such as a volume of biological tissue. In thecontext of biological tissue, a voxel may represent a unit volume of thetissue or a portion of the tissue. For example, in the brain a voxel mayrepresent a unit volume of the brain. Such unit volume may be generatedby a user; for instance, a user may generate the unit volume byselecting one or more parameters in a MRI pulse sequence. In some cases,such unit volume may relate to a biological unit of the tissue. Forexample, a voxel in the context of the brain may correspond to a neuron,a grouping of neurons, one or more brain regions, or one or moreportions of one or more brain regions. A voxel may take a rectilinearform, such as that a cube or rectangular prism. A voxel may be definedby any combination of a first linear dimension, a second lineardimension, and a third linear dimension. The first linear dimension maybe at most 10 μm, at most 20 μm, at most 50 μm, at most 100 μm, at most200 μm, at most 500 μm, at most 1 mm, at most 2 mm, at most 5 mm, or atmost 10 mm. The first linear dimension may have a value that is within arange defined by any two of the preceding values. The second lineardimension may be at most 10 μm, at most 20 μm, at most 50 μm, at most100 μm, at most 200 μm, at most 500 μm, at most 1 mm, at most 2 mm, atmost 5 mm, or at most 10 mm. The second linear dimension may have avalue that is within a range defined by any two of the preceding values.The third linear dimension may be at most 10 μm, at most 20 μm, at most50 μm, at most 100 μm, at most 200 μm, at most 500 μm, at most 1 mm, atmost 2 mm, at most 5 mm, or at most 10 mm. The third linear dimensionmay have a value that is within a range defined by any two of thepreceding values.

Methods for Determining a State of Tissue

In an aspect, the present disclosure provides methods for determining astate of tissue, such as a disorder state of brain tissue. A method fordetermining a disorder state of a tissue in a portion of a body of asubject may comprise obtaining magnetic resonance imaging (MRI) datacomprising at least one MRI image of the tissue. The MRI image maycomprise a plurality of voxels. A voxel of the plurality of voxels maybe associated with the tissue of the subject and comprise one or moremeasured MRI parameters in the MRI data.

Next, for the voxel of the plurality of voxels, one or more computerprocessors may be used to process the one or more measured MRIparameters with one or more simulated MRI parameters for the voxel. Theone or more simulated MRI parameters may be generated from one or moremicrostructural models at the voxel.

Next, for the voxel of the plurality of voxels, a diagnostic model maybe selected from the one or more microstructural models. The diagnosticmodel may be selected using a threshold congruence between the one ormore measured MRI parameters and the one or more simulated MRIparameters associated with the diagnostic model. For the voxel of theplurality of voxels, the diagnostic model may be used to determine thedisorder state of the tissue associated with the voxel.

Methods of the present disclosure may be used to determine a disorderstate of brain tissue of a subject. A method for determining a disorderstate of brain tissue of a subject may comprise obtaining MRI datacomprising at least one MRI image of the brain, the MRI image comprisinga plurality of voxels, a voxel of the plurality of voxels beingassociated with the brain tissue of the brain of the subject andcomprising one or more measured MRI parameters in the MRI data. Next,for the voxel of the plurality of voxels, one or more computerprocessors may be used to process the one or more measured MRIparameters with one or more simulated MRI parameters for the voxel. Theone or more simulated MRI parameters may be generated from one or moremicrostructural models at the voxel. For the voxel of the plurality ofvoxels, a diagnostic model may be selected from the one or moremicrostructural models. The diagnostic model may meet a thresholdcongruence between the one or more measured MRI parameters and the oneor more simulated MRI parameters associated with the diagnostic model.Next, for the voxel of the plurality of voxels, the diagnostic model maybe used to determine the disorder state of the brain tissue associatedwith the voxel.

Reference will now be made to the figures, wherein like numerals referto like parts throughout. It will be appreciated that the figures andelements therein are not necessarily drawn to scale.

FIG. 1 shows a method 100 for determining a disorder state of braintissue in a brain of a subject.

In a first operation 110, the method may comprise obtaining magneticresonance imaging (MRI) data. The MRI data may comprise MRI dataobtained from at least one subject. The MRI data may comprise MRI dataobtained from at least 1, at least 2, at least 5, at least 10, at least20, at least 50, at least 100, at least 200, at least 500, at least1,000, at least 2,000, at least 5,000, at least 10,000, at least 20,000,at least 50,000, at least 100,000, at least 200,000, at least 500,000,or at least 1,000,000 subjects. The MRI data may comprise MRI dataobtained from a number of subjects that is within a range defined by anytwo of the preceding values. The MRI data may comprise at least one MRIimage. The MRI data may comprise at least 1, at least 2, at least 5, atleast 10, at least 20, at least 50, at least 100, at least 200, at least500, at least 1,000, at least 2,000, at least 5,000, at least 10,000, atleast 20,000, at least 50,000, at least 100,000, at least 200,000, atleast 500,000, or at least 1,000,000 MRI images. The MRI data maycomprise a number of MRI images that is within a range defined by anytwo of the preceding values. The MRI data may comprise a single MRIimage of each brain of each subject of the plurality of subjects.Alternatively, the MRI data may comprise a plurality of MRI images ofeach brain of each subject, such as at least 2, at least 3, at least 4,at least 5, at least 10, at least 20, at least 50, at least 100, atleast 200, at least 500, or at least 1,000 images of each brain of eachsubject. The number of images for each subject of the plurality ofsubjects may be the same across all subjects. Alternatively, the numberof images for each subject may differ across the subjects.

One or more of the MRI images may comprise a weighted MRI image. One ormore MRI images may comprise a longitudinal relaxation time(T1)-weighted MRI image. The one or more T1-weighted MRI images may beobtained by a T1-weighted MRI pulse sequence, such as a T1-weighted spinecho pulse sequence, a T1-weighted gradient echo pulse sequence, aparamagnetic contrast agent (such as gadolinium) enhanced T1-weightedpulse sequence, a T1-weighted Fluid-Attenuated Inversion Recovery(T1-FLAIR) pulse sequence, a fat-suppressed T1-weighted pulse sequence,or any other T1-weighted MRI pulse sequence. One or more MRI images maycomprise a transverse relaxation time (T2)-weighted MRI image. The oneor more T2-weighted MRI images may be obtained by a T2-weighted MRIpulse sequence, such as a T2-weighted spin echo pulse sequence, aT2-weighted gradient echo pulse sequence, a T2-weighted Fluid-AttenuatedInversion Recovery (T2-FLAIR) pulse sequence, a fat-suppressedT2-weighted pulse sequence, a T2-star pulse sequence, or any otherT2-weighted MRI pulse sequence. One or more MRI images may comprise adiffusion-weighted MRI image. The one or more diffusion-weighted MRIimages may be obtained by any diffusion-weighted MRI pulse sequence,such as a diffusion-weighted imaging (DWI) pulse sequence, a diffusiontensor imaging (DTI) pulse sequence, or a diffusion kurtosis imaging(DKI) pulse sequence. One or more MRI images may comprise a protondensity (PD)-weighted MRI image. The one or more proton density-weightedMRI images may be obtained by any proton density-weighted MRI pulsesequence, such as to a fat-suppressed proton density-weighted pulsesequence. One or more MRI images may comprise a post-processeddiffusion-weighted image such as an apparent diffusion coefficient (ADC)image, a mean diffusivity (MD) image, an axial diffusivity (AxD) image,a radial diffusivity (RD) image, or a fractional anisotropy (FA) image.The one or more post-processed diffusion-weighted MRI images may beobtained by post-processing of any diffusion-weighted MRI pulsesequence, such as a diffusion-weighted imaging (DWI) pulse sequence, adiffusion tensor imaging (DTI) pulse sequence, or a diffusion kurtosisimaging (DKI) pulse sequence. One or more MRI images may comprise asusceptibility-weight image, a spoiled gradient echo (SPGR) image, afast spoiled gradient echo (FSPGR) image, an inversion recovery spoiledgradient echo (IR SPGR) image, a magnetization prepared rapid gradientecho (MP RAGE) image, or a fluid-attenuated inversion recovery (FLAIR)image. One or more MRI images may comprise a sodium magnetic resonance(sodium MRI) image, a susceptibility-weighted image (SWI), a magneticresonance spectroscopy (MRS) image, a magnetic resonance fingerprinting(MRF) image, a functional magnetic resonance (fMRI) image, such as ablood-oxygen-level-dependent (BOLD) image, or an arterial spin labeling(ASL) image.

Each MRI image may comprise a plurality of voxels. Each voxel may beassociated with brain tissue of the one or more brains of the one ormore subjects. Each voxel may comprise one or more measured MRIparameters. The measured MRI parameters may comprise a measured T1 time,a measured T2 time, a measured proton density, a measured diffusioncoefficient, a measured diffusivity, a measured fractional anisotropy ofdiffusion, or a measured diffusion kurtosis. The measured MRI parametersmay comprise a plurality of measured MRI parameters. For instance, themeasured MRI parameters may comprise a measured T1 time and a measuredT2 time, a measured T1 time and a measured diffusion coefficient, ameasured T2 time and a measured diffusion coefficient, or a measured T1time, a measured T2 time, and a measured diffusion coefficient. Thenumber of measured MRI parameters for each voxel may be the same acrossall voxels, all images, or all subjects. Alternatively, the number ofmeasured MRI parameters for each voxel may differ across the voxels,images, or subjects.

In a second operation 120, the method may comprise using one or morecomputer processors to process the one or more measured MRI parametersfor a voxel of the plurality of voxels. The one or more measured MRIparameters may be processed with one or more simulated MRI parameters.

The simulated MRI parameters may comprise a simulated T1 time, asimulated T2 time, a simulated proton density, a simulated diffusioncoefficient, a simulated diffusivity, a simulated fractional anisotropyof diffusion, or a simulated diffusion kurtosis. The simulated MRIparameters may comprise a plurality of simulated MRI parameters. Forinstance, the simulated MRI parameters may comprise a simulated T1 timeand a simulated T2 time, a simulated T1 time and a simulated diffusioncoefficient, a simulated T2 time and a simulated diffusion coefficient,or a simulated T1 time, a simulated T2 time, and a simulated diffusioncoefficient. The number of simulated MRI parameters for each voxel maybe the same across all voxels, all images, or all subjects.Alternatively, the number of simulated MRI parameters for each voxel maydiffer across the voxels, images, or subjects. The number of simulatedMRI parameters for each voxel may be chosen to equal the number ofmeasured MRI parameters for each voxel.

The one or more simulated MRI parameters may be generated from one ormore microstructural models at the voxel. The microstructural models maycomprise information regarding one or more parameters that may allowcomputation of one or more of the predicted MRI parameters of a voxeldescribed herein. The microstructural models may comprise informationregarding intracellular content of cells that compose brain tissuewithin a voxel, extracellular content of brain tissue within a voxel, adistribution of extracellular content within interstitial space of thebrain tissue within a voxel, a distribution of intracellular contentwithin intracellular space of cells that compose brain tissue within avoxel, or brain tissue geometry.

The microstructural models may comprise measured or predicted values ofone or more microstructural model parameters such as cell density withina voxel, cell shape within a voxel, cell geometry within a voxel, cellsize within a voxel, cell distribution within a voxel, intercellularspacing within a voxel, extracellular matrix homogeneity within a voxel,interstitial tortuosity within a voxel, water to protein ratio within avoxel, water to lipid ratio within a voxel, water to carbohydrate ratiowithin a voxel, protein to lipid ratio within a voxel, protein tocarbohydrate ratio within a voxel, or lipid to carbohydrate ratio withina voxel.

The one or more microstructural models may be informed by knowledge of aregion of the brain in which a given voxel is located. For instance,values of the microstructural model parameters for a voxel associatedwith a particular region of the brain may be assigned based onexperimentally-determined values of the parameters within the givenregion. Alternatively or in combination, values of the microstructuralmodel parameters for a voxel associated with a particular region of thebrain may be assigned based on theoretical predictions of the values ofthe parameters within the given region. In this manner, the one or moremicrostructural model parameters may be dependent on the region of thebrain with which a voxel is associated, and the microstructural modelparameters may be different for other voxels associated with differentregions of the brain.

The one or more microstructural models may be selected from one or moremicrostructural model libraries. Each of the one or more microstructuralmodel libraries may comprise at least 100, at least 200, at least 500,at least 1,000, at least 2,000, at least 5,000, at least 10,000, atleast 20,000, at least 50,000, at least 100,000, at least 200,000, atleast 500,000, or at least 1,000,000 microstructural models. Each of theone or more microstructural model libraries may comprise a number ofmicrostructural models that is within a range defined by any two of thepreceding values. Different microstructural model libraries may be usedto select the one or more microstructural models for a given voxel basedon the region of the brain with which the voxel is associated. The oneor more microstructural model libraries may be constructed using themethod 200 described herein.

The operation 120 may comprise using the one or more computer processorsto process the one or more measured MRI parameters, or a computedfunction or transformation of the one or more measured MRI parameters,with the one or more simulated MRI parameters, or a computed function ortransformation of the one or more simulated MRI parameters, by computingan objective function between the one or more measured MRI parameters,or a computed function or transformation of the one or more measured MRIparameters, and the one or more simulated MRI parameters, or a computedfunction or transformation of the one or more measured MRI parameters,generated from the one or more microstructural models. The objectivefunction may comprise an L1 norm, an L2 norm, or any other objectivefunction.

The objective function may comprise an L1 norm computed between ameasured MRI parameter and a simulated MRI parameter, or an L1 normcomputed between any 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10measured MRI parameters and any 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than10 simulated parameters, respectively. For instance, the objectivefunction may comprise an L1 norm computed between a measured T1 time anda simulated T1 time, an L1 norm computed between a measured T2 time anda simulated T2 time, an L1 norm computed between a measured diffusioncoefficient and a simulated diffusion coefficient, an L1 norm computedbetween a measured T1 time and a simulated T1 time, and a measured T2time and a simulated T2 time, an L1 norm computed between a measured T1time and a simulated T1 time, and a measured diffusion coefficient and asimulated diffusion coefficient, an L1 norm computed between a measuredT2 time and a simulated T2 time, and a measured diffusion coefficientand a simulated diffusion coefficient, or an L1 norm computed between ameasured T1 time and a simulated T1 time, a measured T2 time and asimulated T2 time, and a measured diffusion coefficient and a simulateddiffusion coefficient.

The objective function may comprise an L2 norm computed between ameasured MRI parameter and a simulated MRI parameter, or an L2 normcomputed between any 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10measured MRI parameters and any 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than10 simulated MRI parameters, respectively. For instance, the objectivefunction may comprise an L2 norm computed between a measured T1 time anda simulated T1 time, an L2 norm computed between a measured T2 time anda simulated T2 time, an L2 norm computed between a measured diffusioncoefficient and a simulated diffusion coefficient, an L2 norm computedbetween a measured T1 time and a simulated T1 time, and a measured T2time and a simulated T2 time, an L2 norm computed between a measured T1time and a simulated T1 time, and a measured diffusion coefficient and asimulated diffusion coefficient, an L2 norm computed between a measuredT2 time and a simulated T2 time, and a measured diffusion coefficientand a simulated diffusion coefficient, or an L2 norm computed between ameasured T1 time and a simulated T1 time, a measured T2 time and asimulated T2 time, and a measured diffusion coefficient and a simulateddiffusion coefficient.

The objective function may comprise a weighted L1 norm or a weighted L2norm. The objective function may comprise a Mahalanobis distance. Theobjective function may comprise an explicit formula derived from asingle simulated microstructural model or combinations of simulatedmicrostructural models.

In an operation 130, the method may comprise selecting one or morediagnostic models from the one or more microstructural models for avoxel of the plurality of voxels. The one or more diagnostic models maybe selected by computing an objective function for each of the one ormore microstructural models; the objective function may be any objectivefunction described herein. The objective function for eachmicrostructural model may be tested against a threshold and one or moremicrostructural models may be selected as a diagnostic model if theobjective function for the given microstructural model meets a thresholdcongruence. In some cases, one diagnostic model may be chosen (such asthe diagnostic model which minimizes the objective function). In othercases, a plurality of diagnostic models, such as at least 2, at least 3,at least 4, at least 5, at least 6, at least 7, at least 8, at least 9,at least 10, or at least 10 diagnostic models may be chosen (forinstance, when a plurality of microstructural models meets the thresholdcongruence).

In an operation 140, the method may comprise using the one or morediagnostic models to determine the disorder state of the brain tissueassociated with a voxel of the plurality of voxels. The one or morediagnostic models may indicate a healthy brain tissue state, based onknowledge of the microstructure associated with the diagnostic models.For instance, diagnostic models that are similar in microstructure to aknown healthy microstructure may be indicative of a healthy brain tissuestate; alternatively or in combination, diagnostic models that aredissimilar in microstructure to a known diseased microstructure may beindicative of a healthy brain tissue state. The one or more diagnosticmodels may indicate a diseased brain tissue state, again based onknowledge of the microstructure associated with the diagnostic models.For instance, diagnostic models that are dissimilar in microstructure toa known healthy microstructure may be indicative of a diseased braintissue state; alternatively or in combination, diagnostic models thatare similar in microstructure to a known diseased microstructure may beindicative of a diseased brain tissue state. The one or more diagnosticmodels may comprise qualitative or quantitative information related toan extent to which brain tissue associated with a given voxel hasprogressed from a healthy state to a diseased state.

The selection of a plurality of diagnostic models may serve as a checkas to whether the method has accurately determined the disorder state ofthe brain tissue associated with the voxel. For instance, the pluralityof diagnostic models may be compared to one another. If most or all ofthe plurality of diagnostic models are associated with a healthy brainstate for a given voxel, this may instill greater confidence that themethod has accurately determined that brain tissue associated with thevoxel is healthy. If most or all of the plurality of diagnostic modelsare associated with a diseased brain state for a given voxel, this mayinstill greater confidence that the method has accurately determinedthat brain tissue associated with the voxel is diseased. If theplurality of diagnostic models are not in agreement as to whether thebrain tissue is associated with a healthy or a diseased state, this mayinstill poor confidence that the method has accurately determined thedisorder state of brain tissue associated with the voxel.

The method 100 may be applied to a single voxel of the plurality ofvoxels. The method may be applied to additional voxels of the pluralityof voxels. For instance, operations 120, 130, and 140 may be repeatedone or more times for additional voxels of the plurality of voxels. Themethod may be applied to all other voxels of the plurality of voxels.For instance, operations 120, 130, and 140 may be repeated for all othervoxels of the plurality of voxels. The method may be applied to allvoxels associated with a specified region of the brain. For instance,operations 120, 130, and 140 may be repeated one or more times for allvoxels associated with a specified region of the brain. The method maybe applied to all voxels associated with an entirety of the brain. Forinstance, operations 120, 130, and 140 may be repeated one or more timesfor all voxels associated with an entirety of the brain. The method maybe applied to a plurality of MRI images. For instance, operations 110,120, 130, and 140 may be repeated one or more times for a plurality ofMRI images. Each MRI image of the plurality of images may be associatedwith a brain selected from a plurality of brains. Each brain of theplurality of brains may be associated with a subject selected from aplurality of subjects.

FIG. 2 shows a method 200 for constructing a microstructural modellibrary.

In a first operation 210, the method may comprise creating a firstmicrostructural model corresponding to a brain state that is notassociated with a disorder.

In a second operation 220, the method may comprise iterativelysubjecting the first microstructural model to a perturbation. Eachiteration may produce an additional perturbed microstructural model. Thefirst microstructural model may be selected based on knowledge of thebrain region associated with the voxel. For instance, the firstmicrostructural model may be informed by knowledge of a region of thebrain in which a given voxel is located, as described herein.

The first microstructural model may be subjected to at least 100iterations, at least 200 iterations, at least 500 iterations, at least1,000 iterations, at least 2,000 iterations, at least 5,000 iterations,at least 10,000 iterations, at least 20,000 iterations, at least 50,000iterations, at least 100,000 iterations, at least 200,000 iterations, atleast 500,000 iterations, or at least 1,000,000 iterations to generateat least 100 perturbed microstructural models, at least 200 perturbedmicrostructural models, at least 500 perturbed microstructural models,at least 1,000 perturbed microstructural models, at least 2,000perturbed microstructural models, at least 5,000 perturbedmicrostructural models, at least 10,000 perturbed microstructuralmodels, at least 20,000 perturbed microstructural models, at least50,000 perturbed microstructural models, at least 100,000 perturbedmicrostructural models, at least 200,000 perturbed microstructuralmodels, at least 500,000 perturbed microstructural models, or at least1,000,000 perturbed microstructural models, respectively. The firstmicrostructural model may be subjected to a number of iterations that iswithin a range defined by any two of the preceding values to generate anumber of perturbed microstructural models that is within a rangedefined by any two of the preceding values.

Each iteration may comprise one or more operations that alter one ormore parameters of the first microstructural model or subsequent alterediterations of the first microstructural model. The one or moreoperations may comprise depleting cells, altering cellular morphology ordistribution, altering intracellular or interstitial physico-chemicalcomposition or distribution, altering extracellular matrix compositionor distribution, or altering intercellular spacing. Each iteration maycomprise a stochastic procedure, such as a Monte Carlo procedure.

Many variations, alterations, and adaptations based on the methods 100or 200 provided herein are possible. For example, the order of theoperations of the methods 100 or 200 may be changed, some of theoperations removed, some of the operations duplicated, and additionaloperations added as appropriate. Some of the operations may be performedin succession. Some of the operations may be performed in parallel. Someof the operations may be performed once. Some of the operations may beperformed more than once. Some of the operations may comprisesub-operations. Some of the operations may be automated and some of theoperations may be manual.

The methods 100 or 200 described herein may be used to determine thedisorder state of the brain tissue associated with a voxel at anyaccuracy greater than or equal to 60%, 65%, 70%, 75%, 80%, 85%, 90%,95%, 99%, or greater. The methods may be used to determine the disorderstate of the brain tissue associated with a voxel at an accuracy that iswithin a range defined by any two of the preceding values.

Methods of the present disclosure may be used to determine the disorderstate across the brain tissue associated with a specified region of thebrain at an accuracy greater than or equal to about 60%, 65%, 70%, 75%,80%, 85%, 90%, 95%, 99%, or greater. The methods may be used todetermine the disorder state across the brain tissue associated with aspecified region of the brain at an accuracy that is within a rangedefined by any two of the preceding values.

Methods of the present disclosure may be used to determine the disorderstate across the brain tissue associated with the whole brain of asubject at an accuracy greater than or equal to about 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 99%, or greater. The methods may be used todetermine the disorder state across the brain tissue associated with thewhole brain of a subject at an accuracy that is within a range definedby any two of the preceding values.

Methods of the present disclosure may be used to determine the disorderstate across the brain tissue associated with a plurality of subjects atan accuracy greater than or equal to about 60%, 65%, 70%, 75%, 80%, 85%,90%, 95%, 99%, or greater. The methods may be used to determine thedisorder state across the brain tissue associated with a plurality ofsubjects at an accuracy that is within a range defined by any two of thepreceding values.

Methods of the present disclosure may be used to diagnose anon-neurodegenerative disorder. The non-neurodegenerative disorder maybe a primary neoplasm, a metastatic neoplasm, a seizure disorder, aseizure disorder with focal cortical dysplasia, a demyelinating disease(such as multiple sclerosis), a non-neurodegenerative encephalopathy(such as a hypertensive encephalopathy, an ischemic encephalopathy, ametabolic encephalopathy, or an infectious encephalopathy), acerebrovascular disease (such as stroke or transient ischemic attack),or a psychological disorder (such as schizophrenia, a schizophreniformdisorder, autism, an autism spectrum disorder, depression, bipolardisorder, or obsessive compulsive disorder).

Methods of the present disclosure may be used to diagnose aneurodegenerative disorder. In some cases, the methods may be used todiagnose a neurodegenerative disorder at least 5 years, at least 10years, at least 15 years, or at least 20 years prior to the developmentof symptoms associated with the neurodegenerative disorder. The methodsmay be used to diagnose a neurodegenerative disorder in a time periodprior to the development of symptoms associated with theneurodegenerative disorder that is within a range defined by any two ofthe preceding values. The neurodegenerative disorder may be Alzheimer'sdisease, a non-Alzheimer's dementia disorder, Parkinson's disease, aParkinsonism disorder, a motor neuron disease (such as amyotrophiclateral sclerosis), Huntington's disease, a Huntington's disease-likesyndrome, a transmissible spongiform encephalopathy, chronic traumaticencephalopathy, a tauopathy (such as Pick's disease, corticobasaldegeneration, progressive supranuclear palsy, or Nieman-Pick disease),or any other neurodegenerative disorder.

Methods of the present disclosure may further comprise constructing oneor more brain maps. The one or more brain maps may indicate theneurodegenerative disorder state of the brain tissue associated witheach voxel of a plurality of voxels. The methods may comprise display ofthe one or more brain maps on a graphical user interface (GUI) of anelectronic device of a user.

The one or more brain maps may comprise a qualitative abnormality map(such as a qualitative neurodegeneration map). The qualitativeabnormality map may display whether brain tissue associated with a givenvoxel displays a microstructure consistent with a brain disorder (suchas a neurodegenerative disorder), for each voxel of the plurality ofvoxels. The qualitative abnormality map may be a binary map, with eachvoxel assigned a microstructure consistent with a brain disorderdisplayed in the same color (such as gray or red) on the qualitativeabnormality map. The determination of whether the given voxel displays amicrostructure that is consistent with a brain disorder may be subjectto a thresholding procedure. For instance, a qualitativeneurodegeneration map may only indicate that a given voxel is indicativeof a neurodegenerative disorder if the microstructure associated withthe voxel displays some threshold level of neurodegeneration. Thethresholding procedure may allow a viewer of the qualitativeneurodegeneration map to ignore minimal neurodegeneration and to insteadfocus their attention on more severely compromised areas of the brain.The qualitative abnormality map may be a percent abnormality map (suchas a percent neurodegeneration (PND) map) that indicates a percentage ofa subject's brain (or region of a subject's brain) that displays tissuemicrostructure consistent with a brain disorder (such as aneurodegenerative disorder).

The qualitative abnormality map may indicate whether brain tissueassociated with a given brain region displays a microstructureconsistent with a brain disorder (such as a neurodegenerative disorder),for each brain region of the plurality of brain regions. The qualitativeabnormality map may be a binary map, with each brain region assigned amicrostructure consistent with a brain disorder displayed in the samecolor (such as gray or red) on the qualitative abnormality map. Thedetermination of whether the given brain region displays amicrostructure that is consistent with a brain disorder may be subjectto a thresholding procedure. For instance, a qualitativeneurodegeneration map may only indicate that a given brain region isindicative of a neurodegenerative disorder if the microstructureassociated with the brain region displays some threshold level ofneurodegeneration. The thresholding procedure may allow a viewer of thequalitative neurodegeneration map to ignore minimal neurodegenerationand to instead focus their attention on more severely compromised areasof the brain.

Alternatively or in combination, the one or more brain maps may comprisea quantitative abnormality map, such as a quantitative neurodegeneration(QND) map.

The QND map may display the extent to which brain tissue associated witha given voxel displays a microstructure consistent with aneurodegenerative disorder, for each voxel of the plurality of voxels.The determination of the extent to which the brain tissue associated thegiven voxel displays a microstructure that is consistent with a braindisorder may be subject to a thresholding procedure. The QND map may bea continuous map, with each voxel assigned a microstructure consistentwith a neurodegenerative disorder displayed in a color representing theextent to which the brain tissue at the given voxel has been damaged bythe neurodegenerative disorder on the QND map. For instance, the QND mapmay display voxels associated with brain tissue that shows littleevidence of neurodegeneration displayed in one color (such as blue),voxels associated with brain tissue that shows evidence of extensiveneurodegeneration shown in another color (such as red), and voxelsassociated with brain tissue that shows evidence of intermediateneurodegeneration shown in other colors (such as yellow or orange) basedon the extent to which the brain tissue at the given voxel has beendamaged by the neurodegenerative disorder. Alternatively, the QND mapmay use a gradient of a single color to represent the extent to whicheach voxel has been damaged by the neurodegenerative disorder. The QNDmap may use a gradient of a single color (such as gray) to represent theextent of normal variation in the voxels that have not been damaged bythe neurodegenerative disorder. The QND map may use any color scheme.

The QND map may display the extent to which brain tissue associated witha given brain region displays a microstructure consistent with aneurodegenerative disorder, for each brain region of the plurality ofbrain regions. The determination of the extent to which the brain tissueassociated the given brain region displays a microstructure that isconsistent with a brain disorder may be subject to a thresholdingprocedure. The QND map may be a continuous map, with each brain regionassigned a microstructure consistent with a neurodegenerative disorderdisplayed in a color representing the extent to which the brain tissueat the given brain region has been damaged by the neurodegenerativedisorder on the QND map. For instance, the QND map may display brainregions associated with brain tissue that shows no evidence ofneurodegeneration displayed in one color (such as blue), brain regionsassociated with brain tissue that shows evidence of extensiveneurodegeneration shown in another color (such as red), and brainregions associated with brain tissue that shows evidence of intermediateneurodegeneration shown in other colors (such as yellow or orange) basedon the extent to which the brain tissue at the given brain region hasbeen damaged by the neurodegenerative disorder. Alternatively, the QNDmap may use a gradient of a single color to represent the extent towhich each brain region has been damaged by the neurodegenerativedisorder. The QND map may use a gradient of a single color (such asgray) to represent the extent of normal variation in the brain regionsthat have not been damaged by the neurodegenerative disorder. The QNDmap may use any color scheme.

Methods of the present disclosure may further comprise constructing oneor more data tables. The one or more data tables may indicate theneurodegenerative disorder state of the brain tissue associated witheach voxel of a plurality of voxels. The neurodegenerative disorderstate of the brain tissue associated with each voxel may be representedby a quantitative neurodegeneration (QND) score. Alternatively or incombination, the one or more data tables may indicate theneurodegenerative disorder state of the brain tissue associated with oneor more regions of a brain. The neurodegenerative disorder state of thebrain tissue associated with each region may be represented by a percentneurodegeneration (PND) score and/or a quantitative neurodegeneration(QND) score and/or any other representative independent or compositescore or scores. The one or more data tables may indicate theneurodegenerative disorder state of the entirety of a brain. Theneurodegenerative disorder state of the entirety of a brain may berepresented by a percent neurodegeneration (PND) score and/orquantitative neurodegeneration (QND) score and/or any otherrepresentative independent or composite score. The PND score mayindicate a percentage of a subject's brain (or percentage of a region orregions of a subject's brain) that displays tissue microstructureconsistent with a brain disorder (such as a neurodegenerative disorder).The QND score may indicate the extent to which the brain tissueassociated with a given voxel, or a given region of a subject's brain,or the entirety of a subject's brain, displays tissue microstructureconsistent with a brain disorder (such as a neurodegenerative disorder).

The other representative independent or composite score or scoresdescribed herein may comprise a mathematical combination of multiplemeasures for a given voxel, plurality of voxels, region, plurality ofregions, or whole brains. For example, the composite score may be anestimated neurodegeneration score (END score) comprising a mathematicaloperation of more than one region measure such as the product of PND andQND. The mathematical operation may comprise multiplying PND and QNDscores and dividing the result by 100. The other representativeindependent or composite score or scores may be derived by computingother parameters such as heterogeneity, asymmetry, or clustering of thevoxels or brain regions that display a microstructure that is consistentwith a brain disorder, within a brain region, a plurality of brainregions, or an entirety of a subject's brain. The determination ofwhether a given voxel, a plurality of voxels, a region, a plurality ofregions of a subject's brain or a plurality of subjects' brains displaya microstructure that is consistent with a brain disorder may be subjectto a thresholding procedure. The other representative independent orcomposite score or scores may be derived by computing other parameterssuch as heterogeneity, asymmetry, or clustering of the voxels or brainregions that display a microstructure that is consistent with a healthystate of brain tissue. The other representative independent or compositescore or scores may be a whole brain score (WBS). The WBS may indicatethe extent to which the entirety of a subject's brain displays tissuemicrostructure consistent with a brain disorder (such as aneurodegenerative disorder). The whole brain score may be expressed by areal number. Similarly, the PND score, the QND score, and the otherrepresentative independent or composite score or scores may be expressedby a real number.

Data produced by methods of the present disclosure, alone or incombination with the data tables described herein or the brain mapsdescribed herein, may be analyzed using machine learning procedures toimprove the accuracy of diagnosis of neurodegenerative disorders. Themachine learning procedures may comprise various supervised machinelearning techniques, various semi-supervised machine learningtechniques, and/or various unsupervised machine learning techniques. Forinstance, the machine learning procedures may utilize autoencoders,stacked autoencoders, neural networks, convolutional neural networks,alternating decision trees (ADTree), Decision Stumps, functional trees(FT), logistic model trees (LMT), logistic regression, Random Forests,linear classifiers, factor analysis, principle component analysis,neighborhood component analysis, sparse filtering, stochastic neighborembedding, or any other machine learning algorithm or statisticalalgorithm. One or more algorithms may be used together to generate anensemble method, wherein the ensemble method may be optimized using amachine learning ensemble meta-algorithm such as a boosting (e.g.,AdaBoost, LPBoost, TotalBoost, BrownBoost, MadaBoost, LogitBoost, etc.)to reduce bias and/or variance. Machine learning analyses may beperformed using one or more of various programming languages andplatforms, such as R, Weka, Python, and/or Matlab, for example. Machinelearning analyses may be performed using a machine learning platform,such as BigML.

Methods of the present disclosure may be used to inform drugdevelopment. For instance, the methods may be used to assess theefficacy of pharmaceutical interventions for neurodegenerativedisorders. Since the methods may allow diagnosis of a neurodegenerativedisorder during the earliest stages of the disorder, the methods mayallow pharmaceuticals to be tested on a cohort of subjects at a muchearlier stage in the progression of the neurodegenerative disorder, whenminimal damage to brain tissue has occurred and pharmaceuticalinterventions may be more effective. The methods may allow accuratetracking of neurodegeneration following the administration of apharmaceutical. The methods, alone or in combination with prior methods,may also allow more accurate selection of patients for clinical trials.For instance, the methods may ensure that only those subjects displayingcertain levels or patterns of neurodegeneration are included in a givenclinical trial.

Methods of the present disclosure may enable monitoring of braindisorders (such as neurodegenerative disorders) at a plurality of timepoints, such as at least 2, at least 5, at least 10, at least 20, atleast 50, at least 100, at least 200, at least 500, or at least 1,000time points. The methods may enable monitoring of brain disorders for anumber of time points that is within a range defined by any two of thepreceding values. Each pair of the plurality of time points may beseparated by a plurality of time intervals. For instance, each pair oftime points may be separated by at least 1 day, at least 2 days, atleast 5 days, at least 1 week, at least 2 weeks, at least 1 month, atleast 2 months, at least 5 months, at least 10 months, at least 1 year,at least 2 years, at least 5 years, at least 10 years, at least 20years, at least 50 years, or at least 100 years. Each of the time pointsmay be separated by a period of time that is within a range defined byany two of the preceding values. In this manner, the methods may be usedto track the development or progression of a brain disorder over aperiod of time.

Though described herein with respect to determining a disorder state ofbrain tissue, the methods and systems of the present disclosure, such asthe methods 100 and 200, may be utilized to determine a state (e.g.,disorder state) of other tissues. For instance, methods and systems ofthe present disclosure may be utilized to determine a disorder state ofspinal cord tissue, heart tissue, vascular tissue, lung tissue, livertissue, kidney tissue, esophageal tissue, stomach tissue, intestinaltissue, pancreatic tissue, thyroid tissue, adrenal tissue, spleentissue, lymphatic tissue, appendix tissue, breast tissue, bladdertissue, vaginal tissue, ovarian tissue, uterine tissue, penile tissue,testicular tissue, prostatic tissue, skeletal muscle tissue, skin, ornon-brain tissues of the head and neck (such as soft tissues of theskull base, tissues of facial structures such as the eyes, nose or ears,tissues of the oral cavity such as the tongue, uvula, gingiva, orpalatine tonsils, or deep structures of the neck such as theretropharyngeal space, the para-pharyngeal space, epiglottis, larynx, ortrachea).

Though described herein with respect to analysis of MRI images, themethods and systems of the present disclosure, such as the methods 100and 200, may be utilized to analyze images obtained by other medicalimaging technologies. For instance, the methods may allow analysis ofimages obtained through X-ray computed tomography (CT) imaging, singlephoton emission computed tomography (SPECT) imaging, electronparamagnetic resonance (EPR) imaging, positron emission tomography (PET)imaging, ultrasound imaging, or any combination of such imagingtechnologies.

FIG. 3 shows a system 300 for determining a disorder state of braintissue in a brain of a subject. The system may comprise a database 310.The database may comprise any MRI data described herein. For instance,the database may comprise any MRI data described herein with respect tothe method 100 or the method 200. The system may further comprise one ormore computer processors 320. The one or more processors may beindividually or collectively programmed to implement any of the methodsdescribed herein. For instance, the one or more processors may beindividually or collectively programmed to implement any or alloperations of the methods of the present disclosure, such as methods 100or 200.

FIG. 4 shows a computer system 401 that is programmed or otherwiseconfigured to operate a system or method for determining a disorderstate of brain tissue in a subject described herein. The computer system401 can regulate various aspects of the present disclosure. The computersystem 401 can be an electronic device of a user or a computer systemthat is remotely located with respect to the electronic device. Theelectronic device can be a stationary electronic device such as adesktop computer. The electronic device can be a mobile electronicdevice.

The computer system 401 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 405, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 401 also includes memory or memorylocation 410 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 415 (e.g., hard disk), communicationinterface 420 (e.g., network adapter) for communicating with one or moreother systems, and peripheral devices 425, such as cache, other memory,data storage and/or electronic display adapters. The memory 410, storageunit 415, interface 420 and peripheral devices 425 are in communicationwith the CPU 405 through a communication bus (solid lines), such as amotherboard. The storage unit 415 can be a data storage unit (or datarepository) for storing data. The computer system 401 can be operativelycoupled to a computer network (“network”) 430 with the aid of thecommunication interface 420. The network 430 can be the Internet, aninternet and/or extranet, or an intranet and/or extranet that is incommunication with the Internet. The network 430 in some cases is atelecommunication and/or data network. The network 430 can include oneor more computer servers, which can enable distributed computing, suchas cloud computing. The network 430, in some cases with the aid of thecomputer system 401, can implement a peer-to-peer network, which mayenable devices coupled to the computer system 401 to behave as a clientor a server.

The CPU 405 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 410. The instructionscan be directed to the CPU 405, which can subsequently program orotherwise configure the CPU 405 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 405 can includefetch, decode, execute, and writeback.

The CPU 405 can be part of a circuit, such as an integrated circuit. Oneor more other components of the system 401 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 415 can store files, such as drivers, libraries andsaved programs. The storage unit 415 can store user data, e.g., userpreferences and user programs. The computer system 401 in some cases caninclude one or more additional data storage units that are external tothe computer system 401, such as located on a remote server that is incommunication with the computer system 401 through an intranet or theInternet.

The computer system 401 can communicate with one or more remote computersystems through the network 430. For instance, the computer system 401can communicate with a remote computer system of a user. Examples ofremote computer systems include personal computers (e.g., portable PC),slate or tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab),telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device,Blackberry®), or personal digital assistants. The user can access thecomputer system 401 via the network 430.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 401, such as, for example, on the memory410 or electronic storage unit 415. The machine executable or machinereadable code can be provided in the form of software. During use, thecode can be executed by the processor 405. In some cases, the code canbe retrieved from the storage unit 415 and stored on the memory 410 forready access by the processor 405. In some situations, the electronicstorage unit 415 can be precluded, and machine-executable instructionsare stored on memory 410.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 401, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or “machine readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards, paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 401 can include or be in communication with anelectronic display 435 that comprises a user interface (UI) 440.Examples of UI's include, without limitation, a graphical user interface(GUI) and web-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 405. Thealgorithm can, for example, determine a disorder state of brain tissuein a subject described herein.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

EXAMPLES Example 1: Relation of Microstructure to MRI

FIG. 5 shows the construction of exemplary microstructural models.

The first step is construction of a normal tissue microstructural model.The normal tissue microstructural model includes known measured orpredicted values of cell density, cell shape, cell geometry, cell size,intercellular spacing, extracellular matrix heterogeneity, interstitialtortuosity, water to lipid ratio, and other tissue parameters that caninfluence structural and diffusion measurements. Typical ensemblesconsist of 1024×1024×1024 cell arrays. FIG. 5A shows a portion of a cellarray for a normal tissue microstructural model. As shown in FIG. 5A,gray cubes 510 represent brain cells and green spots 520 representmolecular obstacles of the extracellular space.

The normal tissue microstructural model is then used for finite-elementand Monte Carlo simulations of the tissue chemical composition, tissuemicro-lattice topography, and molecular kinetics, which can subsequentlybe used to generate predicted structural MRI signals (T1-weighted,T2-weighted, and diffusion-weighted MRI signals) and the associated bulkdiffusion coefficients. By modifying the range of input parameters inthe model, the sensitivity of the output signal values is determined.The reconstructed tissue and calculated values can be directlycorrelated to MRI values at a single voxel. FIG. 5B shows a sample MonteCarlo simulation with delivery of freely moving molecules. As shown inFIG. 5B, red dots represent areas of abnormal molecular distribution andblue dots represent areas of normal molecular distribution.

By manipulating structural components independently and in concert (suchas depleting cells, altering morphology, altering interstitialobstructions, etc.) according to reported and predicted variations inbrain tissue, the platform may be used to generate a continuous range oftissue transitions from healthy to severe degeneration. FIG. 5C showsrepresentative models of healthy and degenerating brain tissue.

Through in-silico modeling, a database consisting of many possiblevariations of tissue structure that represent a range of healthy anddiseased states that can be directly translated to MRI scan values hasbeen assembled. With this reference set correlating a variation ofmicrostructure composition to MR signals, real human T1-weighted,T2-weighted and diffusion-weighted MRI scans can be applied to predictthe most probable tissue microstructure contained in each MRI voxel. Theplatform may thus be regarded as providing a virtual tissue microscope.

Example 2: Interpretation of Human MRI

FIG. 6 shows the processing of human MRI images to produceneurodegeneration maps.

With a rigorous pipeline for structural prediction of MRI, each voxel ofthe human brain scan can be comprehensively characterized. The resultingoutput is a gray map with voxel-wise intensity scaling to represent thepredicted deviation from normal.

Based on literature knowledge and initial experimental measurements, itis determined if the calculated tissue structure from a givencombination of T1 time, T2 time, and diffusion coefficient values iswithin tolerance for healthy brain tissue or resides in the spectrum ofabnormality. Each voxel 610 determined abnormal is coded red or avariable color, respectively denoting binary (percent neurodegeneration,PND) or quantitative (quantitative neurodegeneration, QND) abnormalityin output maps. In effect, PND identifies the abnormal voxels (red) andQND defines how abnormal those voxels are. The range is from dark blue620 (close to normal) to light blue 630 (slightly abnormal) to yellow640 (moderately abnormal) to dark red 650 (very abnormal).

Example 3: Generation of PND and QND Brain Maps

FIG. 7 shows exemplary MRI, PND, and QND brain maps from a diseasedindividual. The MRI scan shown in FIG. 7 is a diffusion-weighted imagethat, combined with T1-weighted and T2-weighted images (not shown inFIG. 7 ), is used to generate a grayscale output image. The grayscaleoutput image serves as the underlying image for the PND and QND mapsfollowing removal of the skull and cerebrospinal fluid domains. Redvoxels in the PND map represent microstructures that deviate frompredicted ranges of structural tolerance for normal tissue. Coloredvoxels in the QND map similarly represent abnormal voxels, with coloradded to code for the extent of abnormality (blue=low abnormality,red=high abnormality).

To assess the degenerative state of an individual scan, the abnormalityrelative coverage (PND), extent of degeneration (QND), estimated totaldegeneration (END), variation of gray value (gRou), and variation ofcolor value (color roughness, cRou) were further characterized.

FIG. 8 shows exemplary brain maps for a young, asymptomatic brain, anormal aged brain with no detected neurodegenerative symptoms, and anaged brain with clinical symptoms of severe degeneration. The grayscalemaps reflect determined microstructure at each voxel of eachtwo-dimensional MRI image.

Example 4: Assessment of a Cohort of Subjects

To assess the accuracy of the systems and methods for detectingneurodegeneration described herein, a rigorous microstructure predictionanalysis was performed on available MRI data in the Alzheimer's DiseaseNeuroimaging Initiative (ADNI) database. Scans from healthy individualsand a range of degenerative states, including early and late cognitiveimpairment and diagnosed AD, were blindly processed using the systemsand methods described herein and statistics were generated for eachvoxel of each image slice. Brain output maps were normalized to 50slices for approximate region registration. Mean values were calculatedfor groups segregated by gender, clinical diagnosis, and outputparameter (e.g., PND, QND, END, gRou, and cRou shown in FIG. 9 ). Eachessential evaluation parameter was determined across normalized brainregions and with gender segregation. The population was as follows: 152individuals diagnosed with AD (98 male, 54 female), 507 individualsshowing mild cognitive impairment (MCI) (317 male, 190 female), and 206normal individuals showing no symptoms of cognitive impairment (102male, 104 female)].

FIG. 9 shows mean plots of degenerative analysis output parameters forthe ADNI images. As shown in the mean plots, there is a cleardistinction of patients diagnosed with AD within the PND and roughnessvalues across the scan slices, suggesting a robust use in AD diagnosisconfirmation. As expected from a mix of early and late impairmentstages, the MCI values are shifted toward those of the normalindividuals. Considering the estimate that MCI patients progress at amodest rate (10-15% annually) toward full AD, it is striking that thesystems and methods described herein can discriminate between MCI andnormal patients based on calculated PND and roughness in the femalepatient scans. These raw data are suggestive of an ability to diagnosesome individuals with MCI. The bigger implications, however, are thatsystems and methods for analyzing brain MRI are providing meaningfulestimates of early changes in microstructural state associated withdisease.

Example 5: Diagnosis by Machine Learning

With an extensive list of input parameters and output calculations thatvary across an anatomically variable image stack, the results can bedramatically improved by applying iterative machine learning on a subsetof images. Through machine learning, weighting of known risk factorssuch as age and gender and less obvious regional patterns ofmicrostructure estimates, the distinguishing power of the degenerationmaps can be significantly improved. To validate the diagnostic capacity,the blind ADNI analysis was processed through a BigML algorithm and apredicted diagnosis was assigned to each image set. The resulting dataare presented as a confusion matrix in Table 1.

TABLE 1 Confusion matrix Clinical Diagnosis Disease No disease DiagnosisDisease 643 34 0.95 by BigML No disease 16 172 0.91 0.98 0.83

The sensitivity or ability to positively detect diseased patients usingBigML for categorical prediction (defined here as clinically diagnosedwith either MCI or AD) was 98%. The specificity was 93%. The positivepredictive value was 95%. The negative predictive value was 91%. Overallaccuracy was slightly lower, at 90%, owing to the predicted abnormalityin clinically undiagnosed individuals. Whereas some of these individualsmay not progress toward a clinical diagnosis, there is a possibilitythat the systems and methods described herein are detecting structuralchanges before any cognitive symptoms are present. This populationbordering between normal and abnormal is the most difficult yet arguablythe most important to address.

Example 6: Longitudinal Brain Imaging Studies

To evaluate the ability to predict early onset of degenerative disease,a collection of MRI scans were acquired from collaborators performinglongitudinal brain imaging studies. All patients were imaged at the timeof MCI or AD diagnosis (grouped here as Abnormal, though most patientswere diagnosed with severe MCI), and most received numerous scans priorto symptom presence and clinical diagnosis. Scans were acquired up to 16years prior to diagnosis. The cohort distribution and time of scans,relative to diagnosis date, is shown as a bar plot in FIG. 10 . Includedare a large number of scans from healthy (Normal) individuals withrepeated scans over time (in this case, time 0 is the most recent scanand ‘years before diagnosis’ is years before most recent scan) toreflect the normal individual variation over time.

FIG. 10 shows a population distribution for a longitudinal study toevaluate early detection of Alzheimer's disease. Each of the Abnormalpatients was diagnosed with either MCI or AD. A majority of abnormalpatients were diagnosed with late stage MCI. The scans prior todiagnosis were pooled into 4 year intervals. Segregation by gender showsthe population sizes used for the microstructural degeneration analysis.For the Normal individuals, the time point reflects the time before amost recent scan date.

Plots of the principal output values from the degeneration maps showmarkedly higher PND, END and cRou at the time of diagnosis, as shown inFIG. 11 . Most importantly, the raw output values show impressivedifferentiation of male and female patients relative to normal controlsaverages, even in the earliest scans. The female cohort shows increasedmicrostructure abnormality at the first time interval before diagnosis,but most of the differentiation is lost at earlier time points, possiblydue to a limited sample size or an increasingly lower amounts of, ortotal absence of, microstructural abnormality in very early time points.Remarkably, the male population PND, QND and END remain elevated aboveaverage control values through even the earliest time points. Thisprovides evidence for detection of early degeneration in patients beforeirreversible degeneration occurs and many years before current practiceseffectively diagnose the changes.

FIG. 11 shows an early detection analysis of MRI images collectedlongitudinally from the cohort distribution. Output parameters wereblindly generated through microstructure prediction from input MRI scansat each time point. Bars represent mean values and standard deviationsfor the respective output measure.

To confirm the early detection ability of our microstructure analysis,output evaluation through BigML processing was repeated using differentparameters. After blind processing, each scan was characterized asnormal or abnormal and compared to the known clinical diagnoses. Amongthe 40 diagnosed patients (included here are individuals from the imageset without scans prior to diagnosis), a majority was determined to beabnormal, as shown in FIG. 12 . From this extended cohort, nearly 93%sensitivity was achieved at the time of diagnosis. Remarkably, a similar93% sensitivity was achieved throughout all early scans, includingmultiple scans collected more than a decade before diagnosis, all ofwhich were predicted to be abnormal. Though further detailed evaluationand analysis of newly collected images will be necessary to verify therobustness of our detection system, the initial performance hassurpassed current expectations that exist throughout the field ofclinical brain imaging. Presented herein is a tool with encouragingperformance in detection of early changes in brain tissue structure thatcan serve as a predictor for future development of degenerative disease.This is a tool desperately needed in the medical community andpharmaceutical industry to aid the detection and prevention ofAlzheimer's disease and similar neurodegenerative diseases.

FIG. 12 shows a determination of abnormality in a mixed cohortlongitudinal study. All individuals were diagnosed with abnormalities ofMCI or AD at time 0. Scans prior to diagnosis were grouped in three-yearintervals.

Example 7: Registration of Brain Images

FIG. 13 shows the registration or alignment of subject images to anannotated human brain parcellation atlas. Each row is a subsample ofimages throughout the brain from a different imaging axis ororientation.

Example 8: Region-by-Region Diagnosis

FIG. 14 shows optimized single region prediction accuracy of diagnosiswithin the ADNI dataset for a variety of brain regions using the systemsand methods described herein. Each set of model parameters is tested foroptimal prediction accuracy for each independent brain region.

FIG. 15 shows PND measurement distributions across subjects of a varietyof ages for the whole brain, cerebellum, thalamus, posterior cingulate,precuneus, and hippocampus.

FIG. 16 shows PND measurement distributions across subjects of a varietyof ages for the entorhinal cortex, basal ganglia, parietal lobe,occipital lobe, prefrontal cortex, and premotor cortex.

FIG. 17 shows PND measurement distributions across subjects of a varietyof ages for the precentral gyms, postcentral gyms, temporal lobe,paracentral lobule, olfactory bulb, and anterior-mid cingulum.

Example 8: Machine Learning Prediction from Optimized ADNI ImageProcessing

FIG. 18 shows attainable AD diagnostic metrics using machine learning.Bootstrap-aggregated decision trees were applied to predict hierarchicalclassifiers from optimally processed ADNI images.

Example 9: Whole Brain Scoring

FIG. 19 shows a distribution of whole brain scores (WBS) for ADNIsubject scans. The score for each brain was plotted vs the subject's ageat the time of scan acquisition. In this example, WBSs were generatedthrough logistic regression analysis of regional PND, QND and ENDvalues. The WBS alone provides statistically separated distributions ofnormal and AD-diagnosed individuals.

FURTHER ASPECTS OF THE DISCLOSURE

-   1. A method for determining a disorder state of brain tissue in a    brain of a subject, comprising:    -   (a) obtaining magnetic resonance imaging (MRI) data comprising        at least one MRI image of the brain, the MRI image comprising a        plurality of voxels, a voxel of the plurality of voxels being        associated with the brain tissue of the brain of the subject and        comprising one or more measured MRI parameters in the MRI data;    -   (b) for the voxel of the plurality of voxels, using one or more        computer processors to process the one or more measured MRI        parameters with one or more simulated MRI parameters for the        voxel, the one or more simulated MRI parameters being generated        from one or more microstructural models at the voxel;    -   (c) for the voxel of the plurality of voxels, selecting a        diagnostic model from the one or more microstructural models,        the diagnostic model meeting a threshold congruence between the        one or more measured MRI parameters and the one or more        simulated MRI parameters associated with the diagnostic model;        and    -   (d) for the voxel of the plurality of voxels, using the        diagnostic model to determine the disorder state of the brain        tissue associated with the voxel.-   2. The method of aspect 1, wherein each voxel comprises a plurality    of measured MRI parameters.-   3. The method of aspect 1 or 2, wherein the one or more measured MRI    parameters are a plurality of measured MRI parameters.-   4. The method of any one of aspects 1-3, wherein the one or more    simulated MRI parameters are a plurality of simulated MRI    parameters.-   5. The method of any one of aspects 1-4, further comprising    repeating (b)-(d) one or more times for additional voxels of the    plurality of voxels.-   6. The method of aspect 5, further comprising repeating (b)-(d) for    all other voxels of the plurality of voxels.-   7. The method of aspect 5, further comprising repeating (b)-(d) for    all voxels associated with a specified region of the brain.-   8. The method of aspect 5, further comprising repeating (b)-(d) for    all voxels associated with an entirety of the brain.-   9. The method of aspect 5, further comprising repeating (a)-(d) for    a plurality of MRI images, each MRI image of the plurality of MRI    images associated with a brain selected from a plurality of brains,    each brain of the plurality of brains associated with a subject    selected from a plurality of subjects.-   10. The method of any one of aspects 1-9, wherein the MRI image is    selected from the group consisting of: a longitudinal relaxation    time (T1)-weighted MRI image, a transverse relaxation time    (T2)-weighted MRI image, and a diffusion-weighted MRI image.-   11. The method of any one of aspects 1-10, wherein the measured MRI    parameter is selected from the group consisting of: a longitudinal    relaxation time (T1), a transverse relaxation time (T2), and a    diffusion coefficient.-   12. The method of any one of aspects 1-11, wherein the simulated MRI    parameter is selected from the group consisting of: a longitudinal    relaxation time (T1), a transverse relaxation time (T2), and a    diffusion coefficient.-   13. The method of any one of aspects 1-12, wherein the one or more    microstructural models comprise information regarding a parameter    selected from the group consisting of: intracellular content,    extracellular content, distribution of extracellular content within    interstitial space, distribution of intracellular content within    intracellular space, and tissue geometry.-   14. The method of any one or aspects 1-13, wherein the one or more    microstructural models comprise measured or predicted values of a    parameter selected from the group consisting of: cell density, cell    shape, cell geometry, cell size, cell distribution, intercellular    spacing, extracellular matrix homogeneity, interstitial tortuosity,    water to protein ratio, water to lipid ratio, water to carbohydrate    ratio, protein to lipid ratio, protein to carbohydrate ratio, and    lipid to carbohydrate ratio.-   15. The method of any one of aspects 1-14, wherein the one or more    microstructural models are selected from a microstructural model    library.-   16. The method of aspect 15, wherein the microstructural model    library comprises at least 100 microstructural models.-   17. The method of aspect 15 or 16, wherein the microstructural model    library is constructed by:    -   (a) creating a first microstructural model corresponding to a        brain state that is not associated with a disorder; and    -   (b) iteratively subjecting the first microstructural model to a        perturbation, each iteration producing an additional perturbed        microstructural model.-   18. The method of aspect 17, wherein (b) comprises subjecting the    first microstructural model to at least 100 iterations to generate    at least 100 perturbed microstructural models.-   19. The method of aspect 17 or 18, wherein the first microstructural    model is selected based on knowledge of the brain region associated    with the voxel.-   20. The method of any one of aspects 17-19, wherein the perturbation    comprises an operation selected from the group consisting of:    depleting cells, altering cellular morphology or distribution,    altering intracellular or interstitial physico-chemical composition    or distribution, altering extracellular matrix composition or    distribution, and altering intercellular spacing.-   21. The method of any one of aspects 17-20, wherein the perturbation    comprises a stochastic procedure.-   22. The method of any one of aspects 1-21, wherein the threshold    congruence is determined by computing an objective function between    the one or more measured MRI parameters and the one or more    simulated MRI parameters.-   23. The method of aspect 22, wherein the objective function    comprises an L1 norm or an L2 norm.-   24. The method of any one of aspects 1-23, wherein determining the    disorder state of the brain tissue associated with the voxel is    achieved at an accuracy of at least 90%.-   25. The method of any one of aspects 7-24, wherein determining the    disorder state across the brain tissue associated with the specified    region of the brain is achieved at an accuracy of at least 90%.-   26. The method of any one of aspects 8-25, wherein determining the    disorder state of the brain tissue associated with the whole brain    of the subject is achieved at an accuracy of at least.-   27. The method of any one of aspects 9-26, wherein determining the    disorder state of the brain tissue associated the plurality of    subjects is achieved at an accuracy of at least 90%.-   28. The method of any one of aspects 1-27, wherein the disorder is a    non-neurodegenerative disorder.-   29. The method of aspect 28, wherein the disorder is selected from    the group consisting of: a primary neoplasm, a metastatic neoplasm,    a seizure disorder, a seizure disorder with focal cortical    dysplasia, a demyelinating disorder, a non-neurodegenerative    encephalopathy, a cerebrovascular disease, and a psychological    disorder.-   30. The method of any one of aspects 1-27, wherein the disorder is a    neurodegenerative disorder.-   31. The method of aspect 30, wherein the method enables diagnosis of    a neurodegenerative disorder more than 5 years prior to the    development of symptoms associated with the neurodegenerative    disorder.-   32. The method of aspect 30 or 31, wherein the method enables    monitoring of the neurodegenerative disorder at a plurality of time    points, the plurality of time points separated by a plurality of    time intervals.-   33. The method of any one of aspects 30-32, wherein the    neurodegenerative disorder is selected from the group consisting of:    Alzheimer's disease, a non-Alzheimer's dementia disorder,    Parkinson's disease, a Parkinsonism disorder, a motor neuron    disease, Huntington's disease, a Huntington's disease-like syndrome,    transmissible spongiform encephalopathy, chronic traumatic    encephalopathy, and a tauopathy.-   34. The method of any one of aspects 1-33, further comprising    constructing a brain map that, for each voxel of the plurality of    voxels, indicates the disorder state of the brain tissue associated    with the voxel.-   35. The method of aspect 34, further comprising displaying the brain    map on a graphical user interface of an electronic device of a user.-   36. The method of aspect 34 or 35, wherein the brain map comprises a    qualitative abnormality map.-   37. The method of aspect 34 or 35, wherein the brain map comprises a    binary abnormality map.-   38. The method of aspect 34 or 35, wherein the brain map comprises a    quantitative abnormality map.-   39. The method of aspect 34 or 35, wherein the brain map comprises a    percent abnormality map.-   40. A method for determining a disorder state of a tissue in a    portion of a body of a subject, comprising:    -   (a) obtaining magnetic resonance imaging (MRI) data comprising        at least one MRI image of the tissue, the MRI image comprising a        plurality of voxels, a voxel of the plurality of voxels being        associated with the tissue of the subject and comprising one or        more measured MRI parameters in the MRI data;    -   (b) for the voxel of the plurality of voxels, using one or more        computer processors to process the one or more measured MRI        parameters with one or more simulated MRI parameters for the        voxel, the one or more simulated MRI parameters being generated        from one or more microstructural models at the voxel;    -   (c) for the voxel of the plurality of voxels, selecting a        diagnostic model from the one or more microstructural models,        the diagnostic model meeting a threshold congruence between the        one or more measured MRI parameters and the one or more        simulated MRI parameters associated with the diagnostic model;        and    -   (d) for the voxel of the plurality of voxels, using the        diagnostic model to determine the disorder state of the tissue        associated with the voxel.-   41. The method of aspect 38, wherein the tissue is selected from the    group consisting of: spinal cord tissue, heart tissue, vascular    tissue, lung tissue, liver tissue, kidney tissue, esophageal tissue,    stomach tissue, intestinal tissue, pancreatic tissue, thyroid    tissue, adrenal tissue, spleen tissue, lymphatic tissue, appendix    tissue, breast tissue, bladder tissue, vaginal tissue, ovarian    tissue, uterine tissue, penile tissue, testicular tissue, prostatic    tissue, skeletal muscle tissue, skin, and non-brain tissue of the    head and neck.-   42. A non-transitory computer-readable medium comprising    machine-executable code that, upon execution by one or more computer    processors, implements a method for detecting a disorder state of    brain tissue in a brain of a subject, the method comprising:    -   (a) obtaining magnetic resonance imaging (MRI) data comprising        at least one MRI image of the brain, the MRI image comprising a        plurality of voxels, a voxel of the plurality of voxels being        associated with the brain tissue of the brain of the subject and        comprising one or more measured MRI parameters in the MRI data;    -   (b) for the voxel of the plurality of voxels, using one or more        computer processors to process the one or more measured MRI        parameters with one or more simulated MRI parameters for the        voxel, the one or more simulated MRI parameters being generated        from one or more microstructural models at the voxel;    -   (c) for the voxel of the plurality of voxels, selecting a        diagnostic model from the one or more microstructural models,        the diagnostic model meeting a threshold congruence between the        one or more measured MRI parameters and the one or more        simulated MRI parameters associated with the diagnostic model;        and    -   (d) for the voxel of the plurality of voxels, using the        diagnostic model to determine the disorder state of the brain        tissue associated with the voxel.-   43. The non-transitory computer-readable medium of aspect 42,    wherein each voxel comprises a plurality of measured MRI parameters.-   44. The non-transitory computer-readable medium of aspect 42 or 43,    wherein the one or more measured MRI parameters are a plurality of    measured MRI parameters.-   45. The non-transitory computer-readable medium of any one of    aspects 42-44, wherein the one or more simulated MRI parameters are    a plurality of simulated MRI parameters.-   46. The non-transitory computer-readable medium of any one of    aspects 42-45, wherein the method further comprises repeating    (b)-(d) one or more times for additional voxels of the plurality of    voxels.-   47. The non-transitory computer-readable medium of aspect 46,    wherein the method further comprises repeating (b)-(d) for all other    voxels of the plurality of voxels.-   48. The non-transitory computer-readable medium of aspect 46,    wherein the method further comprises repeating (b)-(d) for all    voxels associated with a specified region of the brain.-   49. The non-transitory computer-readable medium of aspect 46,    wherein the method further comprises repeating (b)-(d) for all    voxels associated with an entirety of the brain.-   50. The non-transitory computer-readable medium of aspect 46,    wherein the method further comprises repeating (a)-(d) for a    plurality of MRI images, each MRI image of the plurality of MRI    images associated with a brain selected from a plurality of brains,    each brain of the plurality of brains associated with a subject    selected from a plurality of subjects.-   51. The non-transitory computer-readable medium of any one of    aspects 42-50, wherein the MRI image is selected from the group    consisting of: a longitudinal relaxation time (T1)-weighted MRI    image, a transverse relaxation time (T2)-weighted MRI image, and a    diffusion-weighted MRI image.-   52. The non-transitory computer-readable medium of any one of    aspects 42-51, wherein the measured MRI parameter is selected from    the group consisting of: a longitudinal relaxation time (T1), a    transverse relaxation time (T2), and a diffusion coefficient.-   53. The non-transitory computer-readable medium of any one of    aspects 42-52, wherein the simulated MRI parameter is selected from    the group consisting of: a longitudinal relaxation time (T1), a    transverse relaxation time (T2), and a diffusion coefficient.-   54. The non-transitory computer-readable medium of any one of    aspects 42-53, wherein the one or more microstructural models    comprise information regarding a parameter selected from the group    consisting of: intracellular content, extracellular content,    distribution of extracellular content within interstitial space,    distribution of intracellular content within intracellular space,    and tissue geometry.-   55. The non-transitory computer-readable medium of any one or    aspects 42-54, wherein the one or more microstructural models    comprise measured or predicted values of a parameter selected from    the group consisting of: cell density, cell shape, cell geometry,    cell size, cell distribution, intercellular spacing, extracellular    matrix homogeneity, interstitial tortuosity, water to protein ratio,    water to lipid ratio, water to carbohydrate ratio, protein to lipid    ratio, protein to carbohydrate ratio, and lipid to carbohydrate    ratio.-   56. The non-transitory computer-readable medium of any one of    aspects 42-55, wherein the one or more microstructural models are    selected from a microstructural model library.-   57. The non-transitory computer-readable medium of aspect 56,    wherein the microstructural model library comprises at least 100    microstructural models.-   58. The non-transitory computer-readable medium of aspect 56 or 57,    wherein the microstructural model library is constructed by:    -   (a) creating a first microstructural model corresponding to a        brain state that is not associated with a disorder; and    -   (b) iteratively subjecting the first microstructural model to a        perturbation, each iteration producing an additional perturbed        microstructural model.-   59. The non-transitory computer-readable medium of aspect 58,    wherein (b) comprises subjecting the first microstructural model to    at least 100 iterations to generate at least 100 perturbed    microstructural models.-   60. The non-transitory computer-readable medium of aspect 58 or 59,    wherein the first microstructural model is selected based on    knowledge of the brain region associated with the voxel.-   61. The non-transitory computer-readable medium of any one of    aspects 58-60, wherein the perturbation comprises an operation    selected from the group consisting of: depleting cells, altering    cellular morphology or distribution, altering intracellular or    interstitial physico-chemical composition or distribution, altering    extracellular matrix composition or distribution, and altering    intercellular spacing.-   62. The non-transitory computer-readable medium of any one of    aspects 58-61, wherein the perturbation comprises a stochastic    procedure.-   63. The non-transitory computer-readable medium of any one of    aspects 42-62, wherein the threshold congruence is determined by    computing an objective function between the one or more measured MRI    parameters and the one or more simulated MRI parameters.-   64. The non-transitory computer-readable medium of aspect 63,    wherein the objective function comprises an L1 norm or an L2 norm.-   65. The non-transitory computer-readable medium of any one of    aspects 42-64, wherein determining the disorder state of the brain    tissue associated with the voxel is achieved at an accuracy of at    least 90%.-   66. The non-transitory computer-readable medium of any one of    aspects 48-65, wherein determining the disorder state across the    brain tissue associated with the specified region of the brain is    achieved at an accuracy of at least 90%.-   67. The non-transitory computer-readable medium of any one of    aspects 49-66, wherein determining the disorder state of the brain    tissue associated with the whole brain of the subject is achieved at    an accuracy of at least 90%.-   68. The non-transitory computer-readable medium of any one of    aspects 50-67, wherein determining the disorder state of the brain    tissue associated the plurality of subjects is achieved at an    accuracy of at least 90%.-   69. The non-transitory computer-readable medium of any one of    aspects 42-68, wherein the disorder is a non-neurodegenerative    disorder.-   70. The non-transitory computer-readable medium of aspect 69,    wherein the disorder is selected from the group consisting of: a    primary neoplasm, a metastatic neoplasm, a seizure disorder, a    seizure disorder with focal cortical dysplasia, a demyelinating    disorder, a non-neurodegenerative encephalopathy, a cerebrovascular    disease, and a psychological disorder.-   71. The non-transitory computer-readable medium of any one of    aspects 42-68, wherein the disorder is a neurodegenerative disorder.-   72. The non-transitory computer-readable medium of aspect 71,    wherein the method enables diagnosis of a neurodegenerative disorder    more than 5 years prior to the development of symptoms associated    with the neurodegenerative disorder.-   73. The non-transitory computer-readable medium of aspect 71 or 72,    wherein the method enables monitoring of the neurodegenerative    disorder at a plurality of time points, the plurality of time points    separated by a plurality of time intervals.-   74. The non-transitory computer-readable medium of any one of    aspects 71-73, wherein the neurodegenerative disorder is selected    from the group consisting of: Alzheimer's disease, a non-Alzheimer's    dementia disorder, Parkinson's disease, a Parkinsonism disorder, a    motor neuron disease, Huntington's disease, a Huntington's    disease-like syndrome, a transmissible spongiform encephalopathy,    chronic traumatic encephalopathy, and a tauopathy.-   75. The non-transitory computer-readable medium of any one of    aspects 42-74, wherein the method further comprises constructing a    brain map that, for each voxel of the plurality of voxels, indicates    the disorder state of the brain tissue associated with the voxel.-   76. The non-transitory computer-readable medium of aspect 75,    wherein the method further comprises displaying the brain map on a    graphical user interface of an electronic device of a user.-   77. The non-transitory computer-readable medium of aspect 75 or 76,    wherein the brain map comprises a qualitative abnormality map.-   78. The non-transitory computer-readable medium of aspect 75 or 76,    wherein the brain map comprises a binary abnormality map.-   79. The non-transitory computer-readable medium of aspect 75 or 76,    wherein the brain map comprises a quantitative abnormality map.-   80. The non-transitory computer-readable medium of aspect 75 or 76,    wherein the brain map comprises a percent abnormality map.-   81. A non-transitory computer-readable medium comprising    machine-executable code that, upon execution by one or more computer    processors, implements a method for detecting a disorder state of a    tissue of a subject, the method comprising:    -   (a) obtaining magnetic resonance imaging (MRI) data comprising        at least one MRI image of the tissue, the MRI image comprising a        plurality of voxels, a voxel of the plurality of voxels being        associated with the tissue of the subject and comprising one or        more measured MRI parameters in the MRI data;    -   (b) for the voxel of the plurality of voxels, using one or more        computer processors to process the one or more measured MRI        parameters with one or more simulated MRI parameters for the        voxel, the one or more simulated MRI parameters being generated        from one or more microstructural models at the voxel;    -   (c) for the voxel of the plurality of voxels, selecting a        diagnostic model from the one or more microstructural models,        the diagnostic model meeting a threshold congruence between the        one or more measured MRI parameters and the one or more        simulated MRI parameters associated with the diagnostic model;        and    -   (d) for the voxel of the plurality of voxels, using the        diagnostic model to determine the disorder state of the tissue        associated with the voxel.-   82. The non-transitory computer-readable medium of aspect 82,    wherein the tissue is selected from the group consisting of: spinal    cord tissue, heart tissue, vascular tissue, lung tissue, liver    tissue, kidney tissue, esophageal tissue, stomach tissue, intestinal    tissue, pancreatic tissue, thyroid tissue, adrenal tissue, spleen    tissue, lymphatic tissue, appendix tissue, breast tissue, bladder    tissue, vaginal tissue, ovarian tissue, uterine tissue, penile    tissue, testicular tissue, prostatic tissue, skeletal muscle tissue,    skin, and non-brain tissue of the head and neck.-   83. A system for determining a disorder state of brain tissue in a    brain of a subject, comprising:    -   (a) a database comprising magnetic resonance imaging (MRI) data        comprising at least one MRI image of the brain, the MRI image        comprising a plurality of voxels, a voxel of the plurality of        voxels being associated with the brain tissue of the brain of        the subject and comprising a measured MRI parameter in the MRI        data; and    -   (b) one or more computer processors operatively coupled to the        database, wherein the one or more computer processors are        individually or collectively programmed to:        -   i. for the voxel of the plurality of voxels, use one or more            computer processors to process the one or more measured MRI            parameters with one or more simulated MRI parameters for the            voxel, the one or more simulated MRI parameters being            generated from one or more microstructural models at the            voxel;        -   ii. for the voxel of the plurality of voxels, select a            diagnostic model from the one or more microstructural            models, the diagnostic model meeting a threshold congruence            between the one or more measured MRI parameters and the one            or more simulated MRI parameters associated with the            diagnostic model; and        -   iii. for the voxel of the plurality of voxels, use the            diagnostic model to determine the disorder state of the            brain tissue associated with the voxel.-   84. The system of aspect 83, wherein each voxel comprises a    plurality of measured MRI parameters.-   85. The system of aspect 83 or 84, wherein the one or more measured    MRI parameters are a plurality of measured MRI parameters.-   86. The system of any one of aspects 83-85, wherein the one or more    simulated MRI parameters are a plurality of simulated MRI    parameters.-   87. The system of any one of aspects 83-86, wherein the one or more    computer processors are further individually or collectively    programmed to repeat (b)-(d) one or more times for additional voxels    of the plurality of voxels.-   88. The system of aspect 87, wherein the one or more computer    processors are further individually or collectively programmed to    repeat (b)-(d) for all other voxels of the plurality of voxels.-   89. The system of aspect 87, wherein the one or more computer    processors are further individually or collectively programmed to    repeat (b)-(d) for all voxels associated with a specified region of    the brain.-   90. The system of aspect 87, wherein the one or more computer    processors are further individually or collectively programmed to    repeat (b)-(d) for all voxels associated with an entirety of the    brain.-   91. The system of aspect 87, wherein the one or more computer    processors are further individually or collectively programmed to    repeat (a)-(d) for a plurality of MRI images, each MRI image of the    plurality of MRI images associated with a brain selected from a    plurality of brains, each brain of the plurality of brains    associated with a subject selected from a plurality of subjects.-   92. The system of any one of aspects 83-91, wherein the MRI image is    selected from the group consisting of: a longitudinal relaxation    time (T1)-weighted MRI image, a transverse relaxation time    (T2)-weighted MRI image, and a diffusion-weighted MRI image.-   93. The system of any one of aspects 83-92, wherein the measured MRI    parameter is selected from the group consisting of: a longitudinal    relaxation time (T1), a transverse relaxation time (T2), and a    diffusion coefficient.-   94. The system of any one of aspects 83-93, wherein the simulated    MRI parameter is selected from the group consisting of: a    longitudinal relaxation time (T1), a transverse relaxation time    (T2), and a diffusion coefficient.-   95. The system of any one of aspects 83-94, wherein the one or more    microstructural models comprise information regarding a parameter    selected from the group consisting of: intracellular content,    extracellular content, distribution of extracellular content within    interstitial space, distribution of intracellular content within    intracellular space, and tissue geometry.-   96. The system of any one or aspects 83-95, wherein the one or more    microstructural models comprise measured or predicted values of a    parameter selected from the group consisting of: cell density, cell    shape, cell geometry, cell size, cell distribution, intercellular    spacing, extracellular matrix homogeneity, interstitial tortuosity,    water to protein ratio, water to lipid ratio, water to carbohydrate    ratio, protein to lipid ratio, protein to carbohydrate ratio, and    lipid to carbohydrate ratio.-   97. The system of any one of aspects 83-96, wherein the one or more    microstructural models are selected from a microstructural model    library.-   98. The system of aspect 97, wherein the microstructural model    library comprises at least 100 microstructural models.-   99. The system of aspect 97 or 98, wherein the microstructural model    library is constructed by:    -   (a) creating a first microstructural model corresponding to a        brain state that is not associated with a disorder; and    -   (b) iteratively subjecting the first microstructural model to a        perturbation, each iteration producing an additional perturbed        microstructural model.-   100. The system of aspect 99, wherein (b) comprises subjecting the    first microstructural model to at least 100 iterations to generate    at least 100 perturbed microstructural models.-   101. The system of aspect 99 or 100, wherein the first    microstructural model is selected based on knowledge of the brain    region associated with the voxel.-   102. The system of any one of aspects 99-101, wherein the    perturbation comprises an operation selected from the group    consisting of: depleting cells, altering cellular morphology or    distribution, altering intracellular or interstitial    physico-chemical composition or distribution, altering extracellular    matrix composition or distribution, and altering intercellular    spacing.-   103. The system of any one of aspects 99-102, wherein the    perturbation comprises a stochastic procedure.-   104. The system of any one of aspects 83-103, wherein the threshold    congruence is determined by computing an objective function between    the one or more measured MRI parameters and the one or more    simulated MRI parameters.-   105. The system of aspect 104, wherein the objective function    comprises an L1 norm or an L2 norm.-   106. The system of any one of aspects 83-105, wherein determining    the disorder state of the brain tissue associated with the voxel is    achieved at an accuracy of at least 90%.-   107. The system of any one of aspects 89-106, wherein determining    the disorder state across the brain tissue associated with the    specified region of the brain is achieved at an accuracy of at least    90%.-   108. The system of any one of aspects 90-107, wherein determining    the disorder state of the brain tissue associated with the whole    brain of the subject is achieved at an accuracy of at least 90%.-   109. The system of any one of aspects 91-108, wherein determining    the disorder state of the brain tissue associated the plurality of    subjects is achieved at an accuracy of at least 90%.-   110. The system of any one of aspects 83-109, wherein the disorder    is a non-neurodegenerative disorder.-   111. The system of aspect 110, wherein the disorder is selected from    the group consisting of: a primary neoplasm, a metastatic neoplasm,    a seizure disorder, a seizure disorder with focal cortical    dysplasia, a demyelinating disorder, a non-neurodegenerative    encephalopathy, a cerebrovascular disorder, and a psychological    disorder.-   112. The system of any one of aspects 83-111, wherein the disorder    is a neurodegenerative disorder.-   113. The system of aspect 112, wherein the system enables diagnosis    of a neurodegenerative disorder more than 5 years prior to the    development of symptoms associated with the neurodegenerative    disorder.-   114. The system of aspect 112 or 113, wherein the system enables    monitoring of the neurodegenerative disorder at a plurality of time    points, the plurality of time points separated by a plurality of    time intervals.-   115. The system any one of aspects 112-114, wherein the    neurodegenerative disorder is selected from the group consisting of:    Alzheimer's disease, a non-Alzheimer's dementia disorder,    Parkinson's disease, a Parkinsonism disorder, a motor neuron    disease, Huntington's disease, a Huntington's disease-like syndrome,    a transmissible spongiform encephalopathy, chronic traumatic    encephalopathy, and a tauopathy.-   116. The system of any one of aspects 83-115, wherein the one or    more computer processors are further individually or collectively    programmed to construct a brain map that, for each voxel of the    plurality of voxels, indicates the disorder state of the brain    tissue associated with the voxel.-   117. The system of aspect 116, wherein the one or more computer    processors are further individually or collectively programmed to    display the brain map on a graphical user interface of an electronic    device of a user.-   118. The system of aspect 116 or 117, wherein the brain map    comprises a qualitative abnormality map.-   119. The system of aspect 116 or 117, wherein the brain map    comprises a binary abnormality map.-   120. The system of aspect 116 or 117, wherein the brain map    comprises a quantitative abnormality map.-   121. The system of aspect 116 or 117, wherein the brain map    comprises a percent abnormality map.-   122. A system for determining a disorder state of a tissue in a    portion of a body of a subject, comprising:    -   (a) a database comprising magnetic resonance imaging (MRI) data        comprising at least one MRI image of the brain, the MRI image        comprising a plurality of voxels, a voxel of the plurality of        voxels being associated with the brain tissue of the brain of        the subject and comprising a measured MRI parameter in the MRI        data; and    -   (b) one or more computer processors operatively coupled to the        database, wherein the one or more computer processors are        individually or collectively programmed to:        -   i. for the voxel of the plurality of voxels, use one or more            computer processors to process the one or more measured MRI            parameters with one or more simulated MRI parameters for the            voxel, the one or more simulated MRI parameters being            generated from one or more microstructural models at the            voxel;        -   ii. for the voxel of the plurality of voxels, select a            diagnostic model from the one or more microstructural            models, the diagnostic model meeting a threshold congruence            between the one or more measured MRI parameters and the one            or more simulated MRI parameters associated with the            diagnostic model; and        -   iii. for the voxel of the plurality of voxels, use the            diagnostic model to determine the disorder state of the            tissue associated with the voxel.-   123. The system of aspect 122, wherein the tissue is selected from    the group consisting of: spinal cord tissue, heart tissue, vascular    tissue, lung tissue, liver tissue, kidney tissue, esophageal tissue,    stomach tissue, intestinal tissue, pancreatic tissue, thyroid    tissue, adrenal tissue, spleen tissue, lymphatic tissue, appendix    tissue, breast tissue, bladder tissue, vaginal tissue, ovarian    tissue, uterine tissue, penile tissue, testicular tissue, prostatic    tissue, skeletal muscle tissue, skin, and non-brain tissue of the    head and neck.

What is claimed is:
 1. A method of determining an efficacy of apharmaceutical intervention for a disorder state of a brain tissue of asubject, comprising: (a) obtaining: (i) a first magnetic resonanceimaging (MM) data comprising at least one first MRI image of the braintissue, the at least one first MRI image comprising a first plurality ofvoxels, a first voxel of the first plurality of voxels being associatedwith the brain tissue and comprising one or more first measured MRIparameters in the first MRI data, and (ii) a second MRI data comprisingat least one second MRI image of the brain tissue, the at least onesecond MRI image comprising a second plurality of voxels, a second voxelof the second plurality of voxels being associated with the brain tissueand comprising one or more second measured MRI parameters in the secondMRI data; (b) for each first voxel of the first plurality of voxels andeach second voxel of the second plurality of voxels, using one or morecomputer processors to process the one or more first and second measuredMRI parameters with one or more first and second simulated MRIparameters generated from one or more first and second microstructuralmodels at the first and second voxels, respectively; (c) for the firstvoxel of the first plurality of voxels and the second voxel of thesecond plurality of voxels, selecting: (i) a first model from the one ormore first microstructural models, the first model meeting a firstthreshold congruence between the first one or more measured MRIparameters and the first one or more simulated MRI parameters associatedwith the first model, and (ii) a second model from the one or moresecond microstructural models, the second model meeting a secondthreshold congruence between the second one or more measured MRIparameters and the second one or more simulated MRI parametersassociated with the second model; (d) using the first model and thesecond model to determine a first disorder state of the brain tissueassociated with the first MRI data and a second disorder state of thebrain tissue associated with the second MRI data, respectively; (e)determining the efficacy of a pharmaceutical intervention, thepharmaceutical intervention administered after obtaining the first MRIdata and prior to obtaining the second MRI data, by comparing the firstdisorder state of the brain tissue and the second disorder state of thebrain tissue.
 2. The method of claim 1, wherein the subject is in anearly stage of a neurodegenerative disorder.
 3. The method of claim 1,further comprising repeating (a)-(d) for a third MRI data, wherein thethird MRI data is obtained after the second MRI data.
 4. The method ofclaim 3, further comprising comparing a third model generated based onthe third MRI data and the second model to determine a longer termefficacy of the pharmaceutical intervention.
 5. The method of claim 1,wherein the one or more first measured MRI parameters or the one or moresecond measured MRI parameters are a plurality of measured MRIparameters.
 6. The method of claim 1, wherein the one or more firstsimulated MRI parameters or the one or more second simulated MRIparameters are a plurality of simulated MRI parameters.
 7. The method ofclaim 1, wherein the one or more first microstructural models or the oneor more second microstructural models comprise information regarding aparameter selected from the group consisting of intracellular content,extracellular content, distribution of extracellular content withininterstitial space, distribution of intracellular content withinintracellular space, and tissue geometry.
 8. The method of claim 1,wherein the one or more first microstructural models or the one or moresecond microstructural models comprise measured or predicted values of aparameter selected from the group consisting of: cell density, cellshape, cell geometry, cell size, cell distribution, intercellularspacing, extracellular matrix homogeneity, interstitial tortuosity,water-to-protein ratio, water-to-lipid ratio, water-to-carbohydrateratio, protein-to-lipid ratio, protein-to-carbohydrate ratio, andlipid-to-carbohydrate ratio.
 9. The method of claim 1, wherein the oneor more first microstructural models or the one or more secondmicrostructural models are selected from a microstructural modellibrary.
 10. The method of claim 9, wherein the microstructural modellibrary is constructed at least in part by: (a) creating a firstmicrostructural model corresponding to a tissue state that is notassociated with the disorder state; and (b) iteratively subjecting thefirst microstructural model to a perturbation, each iteration producingan additional perturbed microstructural model.
 11. The method of claim10, wherein the perturbation comprises an operation selected from thegroup consisting of: depleting cells, altering cellular morphology ordistribution, altering intracellular or interstitial physico-chemicalcomposition or distribution, altering extracellular matrix compositionor distribution, and altering intercellular spacing.
 12. The method ofclaim 10, wherein the perturbation comprises a stochastic procedure. 13.The method of claim 1, wherein the first threshold congruence or thesecond threshold congruence is determined at least in part by computingan objective function between the one or more first measured MRIparameters and the one or more first simulated MRI parameters or betweenthe one or more second measured MRI parameters and the one or moresecond simulated MRI parameters, respectively.
 14. The method of claim13, wherein the objective function comprises an L1 norm or an L2 norm.15. The method of claim 1, wherein the disorder state is selected fromthe group consisting of: a primary neoplasm, a metastatic neoplasm, aseizure disorder, a seizure disorder with focal cortical dysplasia,multiple sclerosis, a non-neurodegenerative encephalopathy, apsychological disorder, Alzheimer's disease, a non-Alzheimer's dementiadisorder, Parkinson's disease, a Parkinsonism disorder, a motor neurondisease, Huntington's disease, a Huntington's disease-like syndrome,transmissible spongiform encephalopathy, chronic traumaticencephalopathy, and a taupathy.
 16. The method of claim 1, furthercomprising constructing a first tissue map or a second tissue map that,for each voxel of the first plurality of voxels or the second pluralityof voxels, indicates the disorder state of the brain tissue associatedwith the voxel, respectively.
 17. The method of claim 1, wherein thefirst MRI image or the second MRI image is selected from the groupconsisting of a longitudinal relaxation time (T1)-weighted MRI image, atransverse relaxation time (T2)-weighted MRI image, and adiffusion-weighted MRI image.
 18. The method of claim 1, wherein thefirst measured MRI parameters or the second measured MRI parameters areselected from the group consisting of a longitudinal relaxation time(T1), a transverse relaxation time (T2), and a diffusion coefficient.19. The method of claim 1, wherein the first simulated MRI parameters orthe second simulated MRI parameters are selected from the groupconsisting of a longitudinal relaxation time (T1), a transverserelaxation time (T2), and a diffusion coefficient.